Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Much of chemical biology is involved with the study of the interactions between small molecules and biomacromolecules, along with any physiological consequences, usually with the aim of finding molecules that are in some senses ‘better’. At a high level, this admits two strategies [1] (Figure 1A). The classical version of chemical genomics was data-driven or ‘function first’; a small molecule was applied to the system of interest (e.g. a rodent inoculated with Mycobacterium tuberculosis) and it either worked (here to kill the bacteria) or it did not. No mechanistic understanding was required (though could later be sought). A major advantage was, after all, that the drug worked. Beyond the thought of trying a variety of molecules, no specific hypothesis was required. In a more modern version, a target (or, much more occasionally a set of targets) is sought, on the basis of a hypothesis, usually about the desirability of inhibiting said target, and typically on a purified protein in vitro. Following a terminology from genetics, the former is referred to as ‘forward’ chemical genomics, the latter as ‘reverse’ (Figure 1A). The nominal advantage of the reverse approach is that in theory one immediately has a mechanism. However, even this is illusory, as effective drugs normally have multiple targets [2], and the ability to bind to a target in vitro conveys little or nothing about its mechanisms, efficacy or toxicity in vivo [3], nor even if it can even reach the supposed target(s) (membrane transporters are normally involved [3–7]). Thus, the search for a molecule with desirable properties (however defined) typically involves a cyclic interplay of the type implied in Figure 1A. As with protein optimisation [8], it is arguably best seen as a navigation through a large search space of possible solutions [9].

Recently, there has been much excitement about the use of methods referred to as ‘Machine Learning’, ‘Artificial Intelligence’, or simply ML or AI. These too can serve to relate the world of ideas to the world of data. Perhaps surprisingly, their relationship to classical (Neyman-Pearson or frequentist) statistics [10] (Figure 1B), is similar to that between forward and reverse chemical genomics (Figure 1A).

The essential problem is that the number of small molecules of potential interest (‘chemical space’) is vast [11–13]. A widely quoted figure, based on simple calculations, albeit dependent on the number of C-atoms considered, is 10⁶⁰ [14,15]. In contrast, the numbers of synthesised and purchasable molecules as recorded at the ZINC database [16] (http://zinc15.docking.org/) are just over 10⁹ and 6.10⁶, respectively (even most of the simple heterocyclic scaffolds have never been made [17,18]). Restricting the number of heavy atoms to just 17, including halogens as well as C, N, O and S, gives more than 10¹¹ molecules [19,20]. This corresponds to an average molecular weight (MW) ∼250 [19], while MWs of 500 and 1000 imply ‘Universes' (for even these restricted chemical spaces) of ca 10³⁶ and ∼10⁷², respectively [15]. An earlier list of 1387 marketed drugs [21] includes over 200 of them (some 15%) with MW exceeding 500 (Figure 2A), while a 2D mapping of ∼6 million ZINC compounds, 150 000natural products, ∼150 fluorophores, ∼1100 endogenous human metabolites (Recon2), and the same marketed drugs (based on [22]) is given in Figure 2B.

Recent advances in computational learning have changed the game of how we can understand and navigate this very large chemical spaces, from a focus on discriminative¹

Because of these very large numbers, that far exceed the capacity of even high-throughput screening assays, virtual screening (VS) has come to the fore. VS [28–31] refers to the use of computational techniques to explore a large compound library in silico, thereby to select a small subset of potentially interesting (here bioactive) molecules that may then be tested experimentally. It is useful to discriminate ‘unsupervised’ from ‘supervised’ learning methods, where in the former we know only the inputs, e.g. molecular structures, without knowing the outputs, e.g. activities of interest. Complementarily, semi-supervised or ‘self-supervised’ [32,33] methods leverage knowledge of the output values (or class labels) where they are known for a subset, while reinforcement leaning methods involve the use of machine learning methods to make decisions based on various kinds of reward. Although VS covers many areas, we shall focus here on Quantitative structure-activity relationships (QSAR) (e.g. [34–36]). QSAR describes a series of techniques for supervised learning, in which we use paired inputs (X) and outputs (Y) (here they are suitably encoded molecular structures and activities, respectively) to produce a model that given the former outputs the latter.

One such means of effecting this mapping is the multilayer perceptron (MLP), in the form of a fully interconnected feedforward artificial neural network [37] (Figure 3A). The MLP consists of nodes (circles) and weights (the lines joining them). Typically in chemical biology and QSAR analyses the inputs (X, just 15 are shown in Figure 3A) would be the values of molecular descriptors or the digits of a binary fingerprint encoding of molecular structure [38]. Outputs (Y) can either be classes or values to be found via a composite nonlinear/mapping function. In the example shown we have six classes. The weights are initialised to small values (typically this was done randomly from a normal distribution; nowadays it is done in a more principled way [39,40]), and a bias term introduced as shown (Figure 3A).

Training such an MLP consists of applying the inputs (usually normalised, commonly in the range 0.1 to 0.9 or 0 to 1 for each) and multiplying them by the relevant weight. A node (shown in the lower right-hand portion of Figure 3A) is a very simple processing unit: it sums the dot product of weights and inputs, then passes it through a transfer function. Classically (in the 1980s/1990s) the transfer function was differentiable, usually something like a hyperbolic tangent function (tanh), that scaled inputs to the range 0 to 1 and passed them to the following layer. In Figure 3A only one ‘hidden’ layer is shown before the output layer. In classification problems it is common to use a Softmax function at the output layer to ensure that the sum of the outputs is 1, thus producing a result vector that represents the probability distribution of the potential outcomes. Training the networks (finding the ‘best’ values for the weights) typically involves an algorithm (‘backprop’) that propagates a partial derivative of the normalised error between the desired and discovered outputs back through the MLP, the weights being adjusted up and down until the outputs have been learned (i.e. can be reproduced when the input is passed forward through the network). Training can be done in batches using subsets of the paired (X-input, Y-output) training data, and each full pass backwards and forwards through the training set is typically referred to as an epoch. Because such networks (of arbitrary size) can learn any arbitrary mapping (known as ‘universal approximation’) they are very prone to overtraining (learning the training set but failing on new inputs), and a separate validation set is used to see when this occurs (so as to prevent it). Finally, a completely independent test set is used that has not been used in training at all. This avoids the (correct) criticism by frequentist statisticians that these methods are effectively testing 1000s of hypotheses in a desperate attempt to try and find one that fits [41].

The key concepts of any kind of learning of this type are that (i) such an MLP can provide sensible outputs on molecules it has never seen (this is referred to as ‘generalisation’), and (ii) the ‘knowledge’ that it has ‘learned’ is effectively stored in the matrix of learned weights. In contrast with say a computer memory, this memory is not held in a single location but in the whole network. This idea (which is also adopted in living organisms) is known as ‘associative’ or ‘content-addressable’ memory, and is the main reason for the ‘robustness’ of structures such as MLPs; destroying some of the connections has little adverse effect on them (and may even improve them — see below). Because the MLP model has learned the general mapping of inputs to outputs by storing it in the matrix of weights, any other reasonable molecule can be applied to it and a suitable output will emanate. This permits any kind of virtual screening for which one has a molecular structure that can be encoded as an input and the potential activity can then be output. Note that here we still have to provide all the molecules as inputs.

While the numbers of inputs and outputs are fixed, there are a number of so-called hyperparameters of an MLP or similar neural network that can improve the speed and effectiveness of learning and generalisation. For the MLP these include the number and size of the hidden layers, the transfer function, the means of weight initialisation, the learning rate, and the so-called momentum (which adds a fraction of the previous weight update to the current one). In addition, one could usefully remove individual nodes or weights that did not seem to contribute to the model [42,43].

Although such MLPs could indeed be used for virtual screening (and many other purposes), they were very slow to train (radial basis function networks [44,45] (as in Figure 3B) were far quicker [46,47]), and it proved impossible to train large nets with even modest (>2) numbers of hidden layers. It is widely considered that this was simply due to the fact that the gradient fed back during the backpropagation step was increasingly small as the number of weights increased (the ‘vanishing gradient’ problem). In other circumstances the gradient could become unstable (the ‘exploding gradient’ problem). Actually, the success of RBF nets gave a strong hint that an unsupervised step prior to (or as part of) the initial weight setting, especially if done layer by layer [48], could be very advantageous.

Another popular neural network architecture of the time was the self-organising feature map popularised by Teuvo Kohonen [49]. It bears some similarities to RBF networks in that it too uses unsupervised methods to cluster the input space prior to the training steps.

At all events, during the 1990s, because of the inability to train large and deep networks, the entire ANN field largely went into stasis save for the activities of a small number of pioneers.

Obviously, this has all changed, since it is now entirely possible to train ANNs that are broadly similar to MLPs but that can have even hundreds of ‘hidden’ layers (e.g. [50]), totalling over a billion interconnections, which is why they are called ‘deep networks’. These very large networks are the basis for the revolution known as ‘deep learning’ [51–53] that underpins widely recognised advances in image and speech recognition and the ability [54] to play board games at absolutely world-leading levels. At the time of writing (September 2020), probably the largest is GPT-3, containing as many as 170 billion weights [55]. Deep learning is now coming to the fore in drug and materials discovery [56–65]. As set out by Geoffrey Hinton, one of those neural network pioneers, the reasons for the original failure were essentially fourfold: (i) the labelled datasets were thousands of times too small; (ii) typical computers used were millions of times too slow; (iii) the weights were initialised in a naive way; (iv) the wrong types of transfer function (non-linearity) were used.

Thus, one major problem with the tanh transfer function (Figure 3A) is that it cannot extrapolate beyond the range on which it has been trained, whereas a rectified linear unit (ReLU) type of transfer function can [66]. This kind of transfer function (Figure 3A), that is zero for all inputs below and including zero and then linear with the sum of the inputs for positive values, is usually far more effective [67], and many others can work well too (e.g. [68,69]). The type of initialisation has also been much improved [39,40], especially using weight initialisations that are based on the size and position of the layer they are in. Finally, the advent of GPU and cloud-based computing has made very powerful computers much more widely available. With this has come the recognition that not only can deep nets ‘store’ more knowledge than can shallow nets, but that they require to be trained on many more input examples. Such large datasets are nowadays commonly available online; when they are not, a useful and effective trick of data augmentation is to add certain kinds of ‘noise’ to those that are [70,71]. This said, a variety of machine learning methods are perfectly capable of learning large datasets for ‘discriminative’ QSAR problems with more or less equal facility [72], and deep learning methods are likely to have only a marginal advantage [73]; a particularly detailed comparison has recently appeared [74].

Although the basic MLP architecture could be modified, it rarely was, and one of the other main features of the deep learning renaissance is the coming to prominence of a series of other architectures. We shall discuss four broad classes: convolutional (CNNs), recurrent (RNNs), long short-term memory (LSTMs), and auto-associative (AA) nets. This will lead us on the variational autoencoder that is our focus here.

The MLP and RBF architectures of Figure 3 used vectors as their inputs, and while it is possible to unfold a 2D matrix (such as an image) pixel by pixel, row-wise or column-wise, to form a vector, this would lose their natural spatial arrangement. (Note that colour pictures with three colour channels RGB actually form a tensor). Similarly, the number of weights (if one weight was used per pixel) would immediately become infeasibly large. Indeed a tiny 10 × 10 image matrix whose pixels could be just black or white ({0,1}) admits 2¹⁰⁰ (∼10³⁰) variants, so some kind of feature extraction is always required [75]. Another of the pioneers of deep learning, Yann LeCun, long recognised the potential utility of ANNs in image recognition [76], and since their invention has been heavily involved in many of the developments of CNNs. Broadly (Figure 4A), a CNN uses a small matrix (e.g. 5 × 5) to range over the larger image, acting as a filter, and passing its output to the next layer, as in an MLP. Following this, the convolution layers are pooled. This strategy decreases both the number of weights and the likelihood of overtraining. Usually, several layers of convolution and pooling are applied, before a final, fully interconnected output layer. Otherwise, training is fairly conventional, using backprop to adjust the weights. They are widely used in image processing, e.g. in radiology [77], cytometry [78], and breast cancer screening [79]. In general, it is considered that the role of the various convolution and pooling layers is precisely to extract and combine features in abstract form (for image recognition tasks it captures the high-level representation of colour, shape etc. in different layers). Some tricks and tips to train CNNs are at https://stanford.edu/∼shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks.

Thus, AtomNet [80] used vectorised versions of 1 Å 3D grids placed over co-complexes of target proteins and small-molecules bound to them that had been sampled within the target's binding site to effect structure-based drug discovery. This said, a simpler representation of a molecule is as a graph (where atoms and bonds become nodes and edges), and there is an equivalent version of a convolutional network for these called, unsurprisingly, a graph convolutional network or GCN [81,82].

Thus far, we have looked only at simple feedforward networks, in which the output of neurons in a given layer acted as input only to neurons in the next layer. While CNNs are arguably the deep network of choice for image processing, many problems such as natural language processing use discrete words in specific sequences. Where the next character (word) is depends on the previous character (word) in a particular word (sentence); in a molecular generation task, the next SMILES character depends in part on the previous character. So instead of a simple input–output transfer function, we need a hidden state with which to try to capture the previous character's information. Here a different kind of architecture is preferred, involving connections between non-adjacent layers, to nodes in the same layer including themselves, and even backwards connections (hence recurrent). A very simple version is shown in Figure 4B. Clearly the number, nature and extent of the ‘recurrent’ connections can be varied considerably. There are different types of RNN to tackle different types of problem with respect to input and output size. These include one-to-many (one input to many outputs) for molecular property to SMILES string generation; many-to-one (multiple input to one output) for SMILES string to prediction of a particular property; and many-to-many (multiple inputs to multiple outputs) e.g. for SMILES character to different molecular properties prediction. The standard backpropagation algorithm has to be modified here, since in unfavourable cases the backpropagation of error could simply be short-circuited. In this case a variant known as ‘backpropagation in time’ is usually used. Even this does not always train well, due to gradient overflow and underflow. However, one specific type of RNN that has come to prominence makes use of a technique that goes back to 1997 [83] but was initially little exploited, known as long short-term memory networks.

LSTMs [83] are a specific type of RNN, and arguably the presently most favoured solution for appropriate problems in deep learning, in which an extra kind of ‘memory’ is explicitly included that effectively helps the recurrent network ‘remember’ its own states from previous epochs during learning, which may include sub-sequences of textual data or previous patterns in time-course data. LSTMs contain special cells (memory cells) with a number of extra parameters that control the functioning of the ‘gates’ (shown in Figure 4C) in the memory cell c_j indicated. The multiplicative gate units open and close access to the flow of constant errors. These extra parameters, not all of which are used in every implementation, provide a very considerable richness of behaviour, whose mathematical intricacies we do not develop further here. They have considerable potential in drug discovery [84]. Recent reviews are at [85,86]. The amount of useful ‘memory’ an LSTM can effectively store is rather limited, and improving this is a particularly active area (see e.g. [87]). A similar RNN known as Gated Recurrent Units (GRUs) [88] has fewer parameters than does LSTM; it has been used in [27] for novel molecule generation.

The last network architecture of immediate interest to us here (Figure 4D) is the autoencoder. On the face of it this is just a simple MLP with one hidden layer whose input and output vectors have the same length, and where it is intended that the weights are evolved to make the output as close to the input as possible. Crucially, the hidden layer is a ‘bottleneck’ layer; what this does is effectively to force the network to abstract the core signal from the input and this makes it resilient to noise in the inputs. The values of the outputs of the bottleneck layer thus provide a representation of the input of a much lower dimensionality, effectively compressing the data, and these may indeed be used for feature representation, clustering, similarity assessment, or data visualisation (somewhat as in Figure 2). This is effectively a kind of principal components analysis (PCA) when there is only one layer with a linear transfer function. Training is normally via standard backpropagation, as in MLPs.

Closely related to autoencoder nets in their appearance (but not at all in their underlying mathematics) are variational autoencoders (Figure 5A) [89–91]. Much of the previous discussion, based implicitly on QSARs, was about what are referred to as discriminative models, where one seeks to learn a predictor from observations (of paired molecules and their activities). What we would really like to have is the ability to generate the molecules themselves ‘de novo’ (e.g. [59,64,65,84,92–107]), by learning what amounts to a joint distribution over all the variables (both inputs and outputs). To this end, a generative model seeks to simulate or recreate how the data are generated ‘in the real world’. Generative models can be trained on existing data and used to generate novel text, images and even music. For our purposes, a generative model can learn to generate molecules that it has never ‘seen’ during the training phase. Of course this means in principle that we can go from the comparatively modest number of molecules that have ever been made and contemplate sampling (generating) them from the much more massive universe (described above) of molecules that might be made. A VAE (Figure 5) consists of two main halves: an encoder (sometimes called a recognition model), and the decoder (which is the generative model). Joining them is a vector (which may be, but does not have to be, of lower dimensionality) that represents the latent space between the two (Figure 5A). A VAE effectively learns stochastic mappings between an observed (input and output) space, whose distribution is provided by the real world, and a latent space that is purposely much simpler and is continuous. Deep learning VAEs are those that are trained using general neural network principles and have multiple ‘hidden’ layers for both the encoding and the decoding. The particular recognition here is that we need to move between the discrete space of molecules (often encoded as SMILES strings [108], but increasingly as molecular graphs) and the continuous space of the neural networks and latent variables of the autoencoders. Fortunately this is now possible, using techniques such as molecular grammars [109,110], and direct graph generation [111–113]. We note that SMILES strings present particular difficulties because their grammar is context-sensitive: making a cut in an arbitrary place in a SMILES string (in contrast with doing so in a protein sequence represented in the one-letter FASTA amino acid code) does not normally lead to two fragments with valid SMILES. (One potential solution to this is to adapt the SMILES grammar to remove the most troublesome elements; DeepSMILES [114] is an example of this.).

A particularly clear example of the utility of generative methods is provided by Gómez-Bombarelli and colleagues [27] (Figure 5B), who encoded and decoded SMILES strings and represented them using a VAE. Since they could easily calculate molecular properties from the SMILES (using RDKit, www.rdkit.org), they also trained a standard MLP to use values of the latent vector as inputs and the calculated properties as outputs. In principle, any kind of deep network might be used for the encoding, and the same or any other kind for the decoding [115]. In this case, the input (encoder) network [27] was mainly a CNN while the output used a specific type of RNN called a gated recurrent unit [116,117]. The latent space used [27] was mainly of 196 dimensions, and the VAE was trained to reproduce its inputs at the outputs (another module from RDKit was used to filter invalid SMILES strings). (Parenthetically, the inputs to be encoded could have been InChI and the outputs decoded as SMILES [118]!)

Now the magic happens. Armed with the ability to generate SMILES strings (and hence molecular structures) from the latent space, the authors could then either perturb the values of latent space vectors away from known molecules, or pick more-or-less arbitrary vectors, see what SMILES strings were generated by the trained decoder, and simultaneously estimate the molecular properties of interest (Figure 5B,C). This allowed them to generate valid molecules with desirable properties (although they did still generate many non-valid SMILES strings). Having a continuous latent space linked to molecular properties (as well as molecular structures) turns the search into an optimisation problem (Figure 5C); many methods are available for such problems [9], and they chose an evolutionary algorithm.

The power of these methods meant that they could also generate chemically valid but potentially bizarre molecules, so the objective function was varied to include high values for the quantitative evaluation of drug likeness [119] (QED) and synthetic accessibility [120] (SA) scores. Although they trained their VAE using only some 250 000 molecules from the ZINC database, they estimated that they could find (i.e. generate) 30 different molecules from any training point in the latent space, implying that their VAE had effectively ‘stored’ the ability to make predictions about 7.5 M molecules. Although there is no simple mapping, molecules that are encoded by vectors that are ‘close’ in the latent space may be expected, to some degree, to be closer in structural space as judged by conventional cheminformatic fingerprints and Tanimoto distances, and hence will tend to have similar properties [121]. Understanding the extent to which this is true this will be of considerable value in the future. Plausibly, even larger nets (or ones with different architectures) trained on much bigger datasets would have generalised to many more molecules. Indeed, Arús-Pous and colleagues [122] trained an RNN with 0.1% of the 675-million GDB-13 database and established that it could generalise to create nearly 70% of GDB-13 while sampling over 2 Bn molecules.

Finally, Figure 6 shows two of these properties of generational networks, using the same VAE as used in Figure 2. In Figure 6A, we take the molecule clozapine that has a particular position in the 100-dimensional latent space. We then perturb this vector randomly by a small amount, read the SMILES strings generated by the decoder (filtering out those that are invalid), and return the molecules so generated. Note that none of these molecules was presented to the network at any point (training, validation or test). Of course ‘similarity’ is in the eye of the beholder, and of the encoding [123], but the top 5 molecules so generated are also ranked and labelled with the Tanimoto similarity to the original molecule as assessed using the RDKit ‘patterned’ encoding, and appear sensible. Similarly, Figure 6B shows some of the drugs ‘encountered’ on a trip between clozapine and prazosin somewhere near the centre of the space depicted in Figure 2. Again, if one is interested in either of these one might then assess such drugs in any assays of interest, having narrowed the search space considerably.

As mentioned, really the key concept in neural networks is their effective ‘storage’ of knowledge as matrices of weights, and their ability to generalise to unseen inputs that are at least reasonably related to those used in their training. Although described in various ways, the bane of neural network training hinges on their tendency to overtrain, i.e. to overfit the training data while losing the ability to predict accurately when presented with new data. Virtually all remedies are designed to minimise this and thus to improve generalisation by ‘sparsifying’ or regularising the trained network. Some such methods include the use of ‘decoys’ (especially generative adversarial networks [53,124,125]), the use of heavy pruning during training, especially ‘dropout’ [126], training in small batches [127], using ensembles of the same network [128], and the addition of noise (even ‘randomised’ SMILES strings [71,117]) to the inputs. It seems that in some cases the values of the hyperparameters are critical, and they interact with each other in complex and hard-to-predict ways (and they may also be optimised using evolutionary algorithms). This makes their training computationally demanding, even before starting to vary the architecture (known as neural architecture search [129–133]). Clearly, there is no limit to the possibilities with which one might combine network architecture modules. Increasing model capacity can either improve or hurt generalisation, depending on the difficulty of the generalisation problem, though the density in latent space is a key determinant of the difficult of a generalisation task [134]. There is a trend towards very large networks that (perhaps unexpectedly [135]) do not overtrain [55]. The biggest and most successful deep networks, presently GPT-3 [55], use transformer [136] architectures, including in drug discovery [137,138]. The largest flavour of GPT-3 has 96 layers with 12 299 nodes in each. At this stage we are not aware of even empirical rules relating e.g. the size of the latent space and the size of the training set of molecules, so an exploratory process, tuned to the problem of interest, is suitable.

The ability to ‘generate’ molecules in silico is in many ways the most exciting of the possibilities opened up by these new methods, since this allows an attack on the ‘inverse’ problem [139] highlighted at the beginning, i.e. to find novel molecules with desirable bioactivities even if those molecules do not themselves appear in existing databases (and of course if they do). Such molecules may not even previously have been synthesised. This is a very active area, so we give just a few examples.

Antimicrobial resistance is a well-known and major problem, and it would be desirable to find new anti-infectives (e.g. [140–142]). Thus, Collins and colleagues [143] trained a deep neural network on 2335 molecules that had been shown experimentally to inhibit the growth of E. coli. The trained network was applied in silico to a variety of other libraries, suggesting that a number of apparently unrelated (and untested) molecules, ones quite different in structure from known antibiotics, might prove to be of value as anti-infectives. One, named halicin (5-{(5-nitro-1,3-thiazol-2-yl)sulfanyl}-1,3,4-thiadiazol-2-amine), was shown to have a broad spectrum of antibacterial activity and appears highly promising. Although halicin is far from their structures, it is noteworthy that nitrofurantoin and metronidazole are two other existing antibiotics with a nitro group on a five-membered heterocycle, and whose mode of action means that resistance is both rare and hard to come by. Specifically, these kinds of molecule seem to have multiple transporters and are reduced to active, radical species.

One feature of in silico analyses is their potential for speed. Nowhere is this more obviously desirable than in the search for safe and effective molecules against coronaviruses, especially that (SARS-Cov-2) causing COVID-19. Shin et al. developed a deep learning strategy termed MT-DTI [144], which they trained on (the then) 97 million molecules in PubChem, and found that they could accurately predict binding affinities based on small molecule structures (transformed from SMILES) and the FASTA amino acid sequences of target proteins within bindingDB. Based on this, Beck and colleagues [145] could predict the potential activity against SARS-CoV-2 of a variety of known antivirals.

The availability of very large experimental datasets [62,146], both online and within companies, will clearly enable much better kinds of virtual screening to be performed, as properties do not simply have to be calculated but can be measured. This said, the increasing power of computers is also bringing forward the ability to calculate many more properties of interest via (time-dependent) density functional theory [147].

Gupta et al. [148] trained a variety of LSTMs in generative molecular discovery, with great success, training on 550 000 SMILES strings from ChEMBL, and generating over 25 000 novel molecules, and many others by growing fragments. Ertl et al. [149] and Segler et al. [150] have used similar strategies for similar purposes, and showed that such methods can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery. Yasonik [97] combined de novo molecular generation in silico (using RNNs) with a multi-objective evolutionary algorithm in an iterative method for selecting suitable molecules subjective to constraints on their physicochemical properties. Finally, here, our own laboratory has developed methods [151] based on molecular graphs and reinforcement learning for generating molecules predicted (from existing binding assays) to have a specific set of differential activities; the methods are entirely general.

For reasons of space, we do not cover in detail a variety of other deep learning areas that may be of interest to readers. However, we give some outlines of strategies in chemical syntheses [152–154] and protein structure prediction [155,156], as well as in optimisation, where deep learning methods are also enjoying considerable success.

Using ‘intelligent’ methods to decide which molecule is of interest is one thing (probably some 10⁶–10⁷ are easily commercially available, depending on the latency of that availability). Using intelligent methods to choose a means by which to make them oneself is entirely another [152–154,157–162]. Probably the present apotheosis of this strategy is Chematica [157,158,163] (recently commercialised as Synthia^TM), that started life as a manually encoded set of reaction rules and now uses (so far as one can tell from published material) a variety of means of intelligent search. The exciting prospect is for the discovery of entirely novel reactions based on those presently known; this is precisely the area in which generative methods can excel.

Leaving aside those that need so-called chaperones, proteins are formed of strings of amino acids (primary structure) that fold up spontaneously to produce the active form of the protein (tertiary structure), which is itself assembled from the coming together of recognisable motifs that include secondary structures such as α-helices and β-sheets. Uniprot https://www.uniprot.org/ (that houses the downloadable set) presently contains some 180 M nonredundant natural protein sequences, increasing at roughly 25 M p.a. (https://bit.ly/2MZekYJ). With an average length of 337 residues, these represent ∼60Bn amino acids. In contrast, the protein databank https://www.rcsb.org/ contains only some 170k 3D structures; since sequencing speeds are far greater than are the methods of structure determination the gap is inevitably going to grow. Consequently, there has long been considerable interest in predicting structure from sequence. Correspondingly, the existence of so many sequences allows the use of unsupervised methods to help to populate the deep learning systems that can then be exploited using supervised methods for the fewer structures there are. Thus, Google DeepMind and collaborators developed Alphafold to optimise the interatomic potentials that control this assembly. They did so by combining three methods: (i) Memory-augmented simulated annealing with neural fragment generation; (ii) memory-augmented simulated annealing with neural fragment generation with distance potential, and (iii) repeated gradient descent of distance potential [156,164]. If the improvements seen during the evolution of their Go-playing reinforcement-learning-based programs [54,165,166] are anything of a guide, we may soon anticipate considerable further improvements. Similar comments might be made about the activities of specific protein sequences [167–170].

Most scientific problems can be cast as combinatorial search problems (‘find me the best experiments to do next out of a potentially vast number') [9]. Even in a simple ‘static’ case where each element can take just M values in an array N of possible parameters, this scales as M^N (e.g 4 bases in a string of 30 = 4³⁰ ∼ 10¹⁸ possibilities [171]). This clearly applies to problems in chemistry and chemical biology that involve navigating large search spaces of molecules, and intelligent automation has been an important subset of activities here (e.g. [172–182]). ‘Active learning’ describes the kinds of methods that use knowledge of existing data to determine where best to explore next, and is normally used to balance exploration (looking for promising regions of the search space) with exploitation (a promising local search) [183]. Of the many strategies for this, evolutionary (‘genetic’) algorithms are pre-eminent, and the area is sometimes referred to as ‘inverse design’ [64]. Where the deep learning meets them is illustrated by the work of Nigam et al. [184], who used a genetic algorithm plus a generative model to optimise the ‘penalised’ logP (J_m), where J_m = logP + SA + RingPenalty and in which logP is the octanol:water partition coefficient, SA is a synthetic accessibility score [120], and RingPenalty adds a penalty for rings larger than six atoms. Other areas have involved organic light-emitting diodes [147] and redox-based batteries [185]. Clearly these methods are generic, and can admit any objective function that can be calculated or measured.

Another important area of optimisation is in microbial biotechnology, whether in finding the best growth medium [186], subsets of genes to manipulate to increase productivity [187,188], or optimal sequences for generating host [189] or protein properties [190,191]. Each of these represents a combinatorial search problem [8,9].

Our last example here involves the search for an optimal (signalling) sequence for effecting protein secretion. Although secretory signals of 15–30 amino acids are more or less well known for systems such as sec [192] and tat [193] in E. coli, natural evolution seems to have performed only a rather limited and stochastic search of these quite large sequence spaces. Thus, Arnold and colleagues [194] used deep learning methods to model known sequences, and could predict novel ones that were ‘highly diverse in sequence, sharing as little as 58% sequence identity with the closest known native signal peptide and 73% ± 9% on average' [194]. These kinds of findings imply strongly that because Nature tends to use weak mutation and strong selection [8], necessarily becoming trapped in local optima, much is to be gained by a deeper exploration of novel sequence spaces.

A widespread and reasonable criticism of these ‘deep learning’ methods is that while they may be good at predicting interesting things, the means by which they do so is entirely opaque [195]. Unravelling this is known as ‘interpretable’ or ‘explainable’ AI. A common view is that ‘disentangling’ the inputs in the encoder of a VAE will lead to a representation in which individual features (or small subsets of features) of the latent space more nearly approximate features of the inputs (e.g. [196–200]). Many flavours do this by adding regulariser terms to the output objective function [201–203], such as in β-VAE [204,205], Deep VIB [206], PixelVAE [207], InfoVAE [208], PRI-VAE [203], VAE-LIME [209], Langevin-VAE [210], Discond-VAE [211], and Gaussian mixture VAE [212], while supervised methods also help with interpretability [213]. The ability to improve our understanding of which parts of a molecule are important for its activity (the ‘pharmacophore’) is particularly prized in drug discovery [214].

Although the future of these kinds of methods is very encouraging generally, it is clear that there are a number of fruitful directions to be explored, beyond the obvious one of novel and better deep learning architectures, algorithms and hyperparameters. One is the representation of small molecules [215], a key to all computational methods, in both 2D and 3D; here the SELFIES approach [216] appears very promising. Another is the use of entirely hardware architectures; these are called neuromorphic computers, and include the spiking neural network engine SpiNNaker [217]. Thus, neural networks effectively amount to (and might loosely be modelled as) electrical circuits. One electrical component that was only recently rediscovered is the memristor; they have been badly underexplored as elements in ANNs but it is easy to predict that this is likely to be an area of fruitful activity [218].

Another area that we find surprisingly underpopulated is that of neuroevolution. While backpropagation is both popular and reasonably well understood, and may even be used in biological learning [219], it is still rather slow. However, adjusting weights (and even architectures) in deep networks to reach a desirable outcome is simply another kind of optimisation problem, that is perfectly amenable to the many flavours of evolutionary computing that work very effectively in optimisation. This has long been recognised [220,221], and since evolutionary algorithms are mostly ‘embarrassingly parallelisable’, we may anticipate considerable advances in the use of neuroevolution for deep learning in chemical biology.

Much of the current activity uses labelled datasets, but far more unlabelled datasets are available. It has been predicted (not least since human learning is mainly unsupervised) [52] that unsupervised learning will play a much larger role in the future, and this seems reasonable. Self-supervised methods [32] seem to show particular promise.

It was always a reasonable view that the much greater ability of humans than machines to store sense data and to reason about them was largely predicated on the simple fact that human brains contain many more neurons. This does now seem to be the case, as very large nets, showing a clear appearance of natural language ‘understanding’ (e.g. [55,222]), become available. They do, however, require very considerable computational resources to train, even with modern GPU-based (and similar) hardware.

With regard to chemical biology, the original motivation of the ‘robot scientist’ project [172] was in fact to conceive and synthesise small molecules; the generative abilities of deep networks now seem capable of bringing this fully closed loop activity within reach [152]. Furthermore, understanding the relationships between molecules that are encoded by vectors in the latent spaces used by deep networks, and their closeness to each other in structural and bioactivity spaces, is likely to be of much value.

From a scientific point of view, the empirical success of deep learning comes at a major cost. That cost, as mentioned, is the cost of knowing precisely how the ‘knowledge’ is actually stored in a deep network, and thus exactly how inputs map to outputs . It is also assumed that such knowledge will help avoid costly and dangerous errors as we begin to trust these methods more. Thus, ‘explainable AI’ will continue to be an important area for the future.

This has been a purposely high-level overview of some of the possibilities in cheminformatics and chemical biology that have been engendered by the development of deep learning methods in general and of generative methods in particular. Our main aim has been to draw attention to these developments, and to some of the means by which readers who are only loosely acquainted with them can incorporate these methods into their own work. Its success will be judged by the rapidity with which this happens.

The authors declare that there are no competing interests associated with the manuscript.

Open access for this article was enabled by the participation of University of Liverpool in an all-inclusive Read & Publish pilot with Portland Press and the Biochemical Society under a transformative agreement with JISC.

Present funding includes part of the UK EPSRC project SuSCoRD [EP/S004963/1], partly sponsored by AkzoNobel. DBK is also funded by the Novo Nordisk Foundation [grant NNF10CC1016517]. We apologise to authors whose contributions were not included due to lack of space.

ANN

artificial neural network

CNN

convolutional neural network

GRU

Gated Recurrent Units

LSTM

long short-term memory

MLP

multilayer perceptron

QSAR

Quantitative structure-activity relationship

RBF

radial basis function

ReLU

rectified linear unit

RNN

recurrent neural network

VAE

variational autoencoder