Zero-Shot 3D Drug Design by Sketching and Generating
Abstract
Drug design is a crucial step in the drug discovery cycle.
Recently, various deeplearning-based methods design drugs by generating novel molecules from scratch,avoiding traversing large-scale drug libraries.
However, they depend on scarceexperimental data or time-consuming docking simulation, leading to overfittingissues with limited training data and slow generation speed.
In this study, wepropose the zero-shot drug design method D ESERT ( D rug d e sign by S k e tchingand gene r a t ing).
Specifically, D ESERT splits the design process into two stages:sketching and generating, and bridges them with the molecular shape.
The twostage fashion enables our method to utilize the large-scale molecular database toreduce the need for experimental data and docking simulation.
Experiments showthat D ESERT achieves a new state-of-the-art at a fast speed.
Introduction
Drug design is a crucial step in the drug discovery cycle, which is the inventive process of finding newdrugs based on a biological target (usually a protein pocket) [1, 2, 3].
However, seeking appropriatedrugs for a particular target is quite challenging due to the enormous space of drug candidates (almost10 33 ).
Traditional drug design approaches usually employ virtual screening [4, 5, 6] and moleculardynamics [7, 8] to traverse in a large scaled drug library, which is time-consuming and could notproduce novel drug candidates.
Recently, a line of work proposes to realize drug design by generatingdrug molecules from scratch using deep generative models [9, 2, 10], which is quite promising due tothe fast speed and the ability of de-novo drug design.
Most of current drug generation model are developed upon 1D (SMILES) [11, 12, 13] or 2D (molecular graph) [14, 15, 16, 17, 18, 19] molecular structures, which heavily rely on expensive experimentaldata for supervised training while ignoring the 3D interaction information between the drug and thepocket.
In a word, they attempt to find a molecule that maximizes the score given by a bio-activity predictor trained on experimental data.
They employ different optimization methods, such as Generative
Adversarial Network (GAN) [20], Bayesian Optimization (BO) [21, 22, 9], Reinforcement Learning(RL) [23, 24, 20, 25, 26, 27], Evolutionary and Genetic Algorithms (EA/GA)
[28, 29, 30, 31, 32],and Markov Chain Monte Carlo (MCMC) [33, 34], in the molecular space for obtaining desireddrug molecule under certain constraints.
However, we argue that a purely data-driven approach ofdrug design is practically limited, since in most cases, the quantity of experimental drug-pocket pairshardly enables supervised training of de-novo drug design.
Generally, most protein pockets lackbio-activity data, and learning on noisy and deficient data may lead to severe overfitting problems.
[Figure]
shape does not change too much ( an less than 1 . 391 Å) and is complemen-
Sketch
Generate
Recently, several drug design models have been proposed to directly generate drug molecules in the3D space, the realistic space of drug-target interaction.
Generating in such space is very promisingfor the potential of leveraging some prior knowledge (e.g., physical knowledge) instead of entirelycounting on data-driven methodology.
Specifically, Masuda et al.
[35] and Luo et al.
[2] proposeefficient 3D generative models to learn the atom density conditioned on protein pockets, with GANand auto-regressive models, respectively.
Nevertheless experimental data are still obligatory in theirmodels for achieving satisfactory results.
Even more noteworthy is GEKO [10], which combines theideas of 3D generation and physical simulation to obtain state-of-the-art drug design performancewithout the help of large-scale experimental data.
Intuitively, GEKO performs geometric editingin the 3D molecule space guided by docking simulation (physical knowledge) [36, 37].
However,
GEKO may suffer from two concerns: a) the frequent invocation of docking is very time consuming,which significantly slows down the speed of the drug design model [38, 39].
b) docking may notalways be accurate enough, especially in some complex settings [40, 41].
In such a case, beingheavily dependent on the docking accuracy could hurt the generalization of the proposed drug designmodel.
In this paper, we propose the zero-shot approach D ESERT , namely D rug d e sign by S k e tching andgene r a t ing.
Motivated by the idea of structure determines properties [42, 43, 44, 45], D ESERT isbuilt on the assumption that molecular shape determines bio-activity between drug molecules and itstarget pocket.
In other words, we suppose that a drug candidate would have satisfactory bio-activityto a target pocket if their shapes are complementary (see Figure 1).
With such prior, as shown in Figure 2, D ESERT splits the whole drug design process into twostage: sketching and generating, which employs the molecular shape as the bridge of the two stages.
Such splitting makes D ESERT enjoy two advantages: a) D ESERT does not heavily rely on dockingsimulation, which only optionally uses docking for post-process and thus avoids the aforementioneddisadvantages of GEKO.
b) D ESERT abandons the expensive experimental data.
Specifically, inthe sketching stage, we only need to sample some reasonable shapes complementary to the targetpocket.
In the generating stage, D ESERT proposes to employ a generative pre-trained model (fromshape to concrete molecular) to fill the shape obtained in the last sketching stage.
Notably, thegenerative pre-trained model is only trained on the ZINC database, which contains 1000M pairs ofmolecules and their corresponding shapes.
This process does not rely on experimental data, making
D ESERT work in a zero-shot fashion.
Note that D ESERT is not baseless.
Besides the idea that structure determines properties (an importantconcept of Structural Biochemistry), we also have preliminary results to verify our assumption.
Figure 1 shows that after binding to a pocket, the root-mean-square deviation (RMSD) is not tooshabby ( 1 . 391 Å) compared with molecular conformation generation methods such as CGCF [46]( 1 . 248 Å) and ETKDG [47] ( 1 . 042 Å).
The results suggest that the molecular shape is stable whenmolecules bind to proteins.
We also show in Figure 1 that a ligand often attaches tight to a pocket,which means their shape is complementary.
Training
Reconstructed Molecules …
S h a p e E n c o d e r 3 D M o l e c u l e
D e c o d e r
Shape2Mol MoleculeShapes … 1000M
[Figure]
molecules
Sketching
S h a p e E n c o d e r 3 D M o l e c u l e
D e c o d e r
Shape2Mol
Generating
DesignedMolecule
Designing (Ligand-based) (
[Figure]
sketching
Sketching
S h a p e E n c o d e r 3 D M o l e c u l e
D e c o d e r
Shape2Mol
Generating
DesignedMolecule
Designing (Pocket-based)
[Figure]
In summary, our contributions are three-fold: (1) We propose D ESERT , a generative method forde-novo 3D shape-based drug design in the zero-shot setting.
(2) D ESERT trains a pre-trained modelfrom massive unbound molecules 2 , eliminating the constraints of labeled data.
(3) D ESERT achievesa new state-of-the-art result (the docking score improves 0 .
79kcal / mol and 2 .
93kcal / mol over thebest supervised method on two datasets) at a fast speed (about 20 times faster than GEKO 3 ).
Proposed Method: D ESERT
In this section, we describe the proposed method in detail.
Inspired by the two aforementionedpreliminary studies (see Figure 1 and Figure 2), D ESERT designs drugs for proteins in a two-stagefashion and employs the shape as a bridge since previous work has shown the feasibility of designingdrug by molecular shape [49, 50, 51, 52, 53].
Specifically, D ESERT designs drugs by first sampling appropriate shapes complementary to the targetpocket and then mapping the shapes to specific molecules.
In Section 2.1, we first introduce theoverall picture of how D ESERT works in a zero-shot setting.
Then we pose two challenges: how tosketch the reasonable molecular shapes and how to generate corresponding molecules based on theshapes.
We put forward solutions in Section 2.2 and Section 2.3, respectively.
2.1 Zero-Shot Pipeline
In this section, we focus on how D ESERT designs drugs in the zero-shot setting.
Briefly speaking,
D ESERT produces molecules in a two-stage fashion: sampling the shape of the desired drug first(sketching) and generating molecules conditioned on the resulting shape (generating).
Zero-Shot Sketching There are mainly two cases when D ESERT needs to sample molecular shapes.
In the zero-shot case where no reference protein ligands are available, D ESERT samples reasonableshapes from protein pockets (see Figure 3c) based on biological observations.
Besides, D ESERT canalso reuse the shape of a ligand to design a novel one (see Figure 3b).
Details are listed in Section 2.2.
Zero-Shot Generating D ESERT generates molecules through a pre-trained generative model, namely
S HAPE 2M OL , which can convert a given shape into diverse molecules (see Figure 3a).
In thisprocedure, we utilize massive unbound molecules to train the model.
Thus no information aboutproteins is needed.
Details of this model are presented in Section 2.3.
[Figure]
2.2 Sketching Molecular Shapes
[Figure]
The sketching stage is responsible for deciding what desired molecules look like.
In this section, weshow how we design two heuristic methods to sketch the shapes of desired molecules.
[Figure]
There are mainly two cases when we sketch a molecule shape based on whether the ligand is provided.
[Figure]
When the ligand is available, the sketching process can be trivial since molecules with similar shapeshave similar properties.
It is reasonable to directly use the ligand’s shape as the shape of the desiredmolecule, which we call Ligand-based Sketching .
If the ligand of the pocket is unavailable, thechallenge is how to obtain a shape that has a high potential to bind to a pocket.
4 We name this Pocketbased Sketching , and our main idea is to sample a region with an appropriate size complementary tothe surface of the pocket.
Our idea for Pocket-based Sketching is based on two main observations:
Ligands mainly lie in the area close to the pocket surface.
Figure
shows that the shape of
satisfactory ligand is tightly complementary to the pocket.
Pockets are usually much larger than ligands, suggesting that directly utilizing the shape of
pockets to design molecules is inappropriate
To this end, we present an algorithm (see the Appendix 1.1) to obtain the desired molecule shape,which is of the appropriate size and complementary to the pocket surface.
We achieve this goal byfinding another shape (namely seed shape) that intersects with the pocket, where the intersection hasa similar size to a molecule.
We also show a 2D illustration in Figure 4.
2.3 Generating 3D Molecules by S HAPE 2M OL
In this section, we introduce S HAPE 2M OL , an encoder-decoder network mapping a shape to diverseand high-quality 3D molecules.
There are plenty of unbound molecule data, e.g., 1000M moleculesin the ZINC database, making it possible to learn a large-scale pre-trained generative model fromshape to molecule.
Concretely, we formulate the problem as an image-to-sequence generation, where the shape isvoxelized as a 3D image (see 2.3.1), and the 3D molecule is converted to be a sequence (see2.3.2).
Our generative approach is capable of modeling any complicated molecule structure and thelinearization makes large-scale pre-training easier to implement.
2.3.1 Encoder: Voxelized Shape
The shape encoder is a 3D extension of the ViT [56], where we use 3D patches instead of 2D patches in the original ViT. Let A denote the set of all atoms, a molecule m can be constructed as a collection of atoms and their corresponding coordinates:
[Equation]
[BOS]
T
T
T… …
T n
T nT n [EOS]
T[BOS], T 1 , T 2 , [BOB], T 3 , [EOB], [BOB], T 4 , T 5 , [EOB], T 6 , T 7 , T 8 , [EOS]
T T
T T
T
T 7 T
Molecule
Tree Structure
Fragment Sequence
Tokenization
Linearization
Given a molecule m , we transform its shape into a 3D image with a voxelization function v m : Z 3 → { 0 , 1 } :
[Equation]
where r denotes the Van der Waals radii [57], ϵ is a perturbed noise which helps prevent overfitting[58].
2.3.2 Decoder: Linearized Molecule
The molecule decoder in our model is similar to the Transformer decoder in machine translation [59].
The main difference is that the decoding object here is a 3D molecule instead of a 1D sequence.
Toaddress this, we propose a 3D molecule decoder, which handles a 3D molecule as a sequence oftuples.
The sequence object eases the implementation of a pre-trained model.
To obtain the object,we first cut a molecule into pieces, then convert it to a sequence.
Tokenization We cut a molecule into pieces so that the generative process can be easily factorized.
Our principles are three folds: (i) preserving the functional groups since they are vital for determiningmolecule properties [60], (ii) avoiding too large size of the vocabulary to ease the pre-training process[61], (iii) no circles exist in the segmented molecules since a tree structure is simpler to handle than agraph.
Our method is simple yet efficient: first tokenizing the molecules with BRCIS [62] and thencutting all single bonds attached to a ring.
More details about some pilot experiments can be found in
Appendix 1.2.
Linearization For the network output, we propose to utilize a linearized sequence to represent thetarget molecule graph, which is not only convenient for training, but also has the strong power torepresent any complicated tree structure.
We first select an fragment whose degree is 1 as the root ofthe tree (e.g., T1 , T3 , T5 , and T8 in Figure 6).
Then we traverse the tree in the depth-first-traversestyle[63].
Whenever we enter or leave a branch, we will add two special symbols [BOB] and [EOB](beginning/ending of a branch), respectively.
More particularly, we use a tuple ( C, P, R ) to represent a fragment F , where C = 1 F is an indicator function [64] denoting its index in the vocabulary, P ∈ R 3 is the translation vector [65],
R ∈ Ris the rotation quaternion [66].
In order to stabilize the training process, we further discretize the continuous variable P and R into P c and R c , respectively.
Taking the translation vector P as an example, we convert it into a binary vector P c , which satisfies:
[Equation]
where L is the max translation length, b is the bin size.
2.4 Training & Decoding
Training Given the output probabilities of the model ( ˆ C i , ˆ P ci , ˆ R ci ) , which denote the probability ofa fragment, a discreterized translation vector, and a discreterized rotation vector, respectively.
Wecalculate the corresponding cross-entropy loss and use their sum as the final loss function.
[Equation]
where n is the length of fragment sequence.
We use 100M unbound molecules sampled from the lead-like subset of ZINC as the training data.
The Transformer’s dimension is 1024, and both encoder and decoder have 12 stacked Transformer layers.
When training S HAPE 2M OL , we set the dropout rate as 0.1, batch size 2048, train step 300K and use AdamW [67] with learning rate 5e-4, weight decay 1e-2, and warmup step 4000 as the optimizer.
The model is trained on 32 Telsa V100 GPU cards for 2 weeks.
Following [35], we also randomlyrotate and translate the input shape for invariance.
Decoding We design a decoding strategy to provide diverse and high-quality candidate moleculesfor a given protein.
To achieve diversity, we employ the sampling method Nucleus [68] to generatemultiple fragment sequences.
Then we convert the sequences back to molecules with a greedyalgorithm (see Appendix 1.2), which connects the fragments by greedily enumerating the nearest pairof breakpoints.
Finally, we do some post-processing operation to further improve the diversity andquality.
We remove the duplicate molecules and leverage the docking simulation to drop moleculesthat do not pass the affinity threshold.
When testing S HAPE 2M OL , we set the threshold of Nucleus sampling to 0.95.
For each proteinpocket, we sketch 200 shapes.
For each shape, we generate 1000 molecules.
More details of
S HAPE 2M OL can be found in the Appendix 1.3.
Results and Discussions3.1 ExperimentsData
We evaluate the performance of our method on drug design by using a total of 12 proteins(PDB IDs: 1FKG, 2RD6, 3H7W, 3VRJ, 4CG9, 4OQ3, 4PS7, 5E19, 5MKU, 3FI2, 4J71), which isa combination of the test data used in Masuda et al.
[35] and Jin et al.
Among the proteins,only JNK3 and GSK3 β (PDB IDs: 3FI2, 4J71) have sufficient labeled data to train well-performingbioactivity predictors.
We denote the set of these two proteins as Set B and the rest as Set A. Because1D/2D methods need bioactivity predictors to design drugs for given proteins, we only evaluate themon Set B while evaluating 3D-based methods on both sets.
Baselines
We compare D ESERT with some baselines for drug design.
Based on the resourceneeded for designing drugs, we further divide these baselines into three groups: Guided methodsneed docking simulation or extra bioactivity predictors to provide supervision signals.
Supervisedmethods rely on labeled data to train their models.
Retrieved methods directly search the databasefor desired molecules.
Evaluation
Following Yang et al.
[10], we evaluate the performance of methods from twoaspects.
(1) the molecular space covered by designed results.
(2) the capacity to provide highly [Table] active molecules.
As a high-quality molecular space should contain diverse, novel molecules withpharmaceutical potential, we use five metrics to evaluate the molecular space: Uniqueness ( Uniq ),
Novelty ( Nov ), Diversity ( Div ), Success rate ( Succ ), and Product ( Prod ).
To evaluate the ability toprovide highly active molecules, we compare the distributions of Vina score and use Median Vina
Score ( Median ) to quantify the distribution.
More details about these metrics can be found in
Appendix 2.1.
Detail of Generation We describe how different methods generate molecules for comparison.
Foreach protein, every method needs to generate 100 molecules for comparison.
For 3D methods, wefurther use the local minimization module in Vina [36] to optimize the generated structures.
For1D/2D methods, we first use RDKit to generate the 3D conformer of their results.
For method
S CREEN , we sample 1K and 200K molecules from ZINC and return 100 molecules with the highest
Vina score as the generated results.
3.2 Main Results
Table 1 shows the results of D ESERT and baselines.
All values are averaged over target proteins.
Ourmain findings are listed as follows:
I.The zero-shot D ESERT achieves the SOTA result at a fast speed
D ESERT ’s performance is strongcompared with GEKO, an MCMC-based model with a huge sampling space.
Compared with GEKO,which works in a trial-and-error way, D ESERT makes a more clever choice by pruning the space withits biological knowledge regarding the shape and quickly finds a good solution with limited hintsfrom a teacher.
We compare the generation speed in Figure 7.
II.The shape helps D ESERT produce high quality molecules.
The molecular space of S CREENis the ZINC database, while that of D ESERT is generated by the pre-trained model S HAPE 2M OL ,which is aware of the pocket’s shape.
According to the table, both D ESERT -L IGAND and D ESERT -
[Figure] [Figure]
P OCKET show observably better performance than their counterparts, i.e., S CREEN (1K) and S CREEN(200K).
III. More comprehensive exploration of protein pockets benefits performance.
Instead of using themolecular shapes of reference ligands as input, D ESERT -P OCKET comprehensively explores theprotein pockets by sampling multiple molecular shapes from them.
Therefore, it has the potential toobtain diverse, high-quality molecules that bind to protein pockets in different regions.
While D ESERTL IGAND only considers one region, i.e., the region that ligands lie in, limiting the exploration ofprotein pockets.
We show a case in Figure 8.
IV.
Unsupervised methods have larger potential than the supervised counterparts.
As labeled datais inadequate, e.g., scPDB 6 only has 16,034 entries, the supervised methods easily collapse to themain molecule pattern in their dataset.
When generating molecules with them, we often get the samemolecules, e.g., 3D SBDD can only generate 16 unique molecules for protein 4OQ3.
D ESERT utilizesmassive unbound molecules, which leads to the learned space being denser.
Combing with anappropriate sampling method, it can generate diverse molecules.
3.3 Comparison with Related Shape-based Models
In this section, we study the shape faithfulness and structure rationality compared with previousshape-based models.
In Table 2, we use Shape Tanimoto 7 to evaluate the faithfulness and use
Free Energy to quantify the rationality [73].
We compares D ESERT -L IGAND with liGAN (Ligand),a variant of liGAN [35] which utilizes the existing ligands.
Although liGAN (Ligand) achieveshigh performance on Shape Tanimoto, its atom-based decoding strategy does not guarantee thecorrect relative position between atoms.
Therefore, liGAN shows higher Free Energy, indicating thatunrealistic structures may appear.
Figure 9 shows a case where liGAN produces an ill ring structure.
[Table]
[Figure] [Figure]
3.4 Ablation Study of Generating
In this section, we evaluate several designs of the generating stage in our D ESERT method, whichrelate to the pre-trained model and decoding strategy.
All results are based on an extra test set from
ZINC and a smaller version of S HAPE 2M OL .
Pre-training Configuration We evaluate the model quality on different pre-training configurations(mainly focusing on the size of the model and training data).
The results in Figure 10 show: (1)
Larger model achieves better performance.
Devlin et al.
[74] observes similar phenomenons in naturallanguage modeling.
(2) Performance saturation occurs when the dataset is of moderate size.
As themap from radii to atoms is easy to learn, the model can capture it with a moderate dataset.
Liu et al.[75] reports a similar result that a large dataset does not necessarily lead to better quality.
More Ablation We also do ablation study about some model variants (including discretization androbust training ) and decoding strategies .
We further study chemical information driven design .
Werefer the reader for more details to the Appendix 2.2.
3.5 Ablation Study of Sketching
In this section, we study the sketching stage in our D ESERT method, which includes the effect ofsampling space size and seed shape on the method’s performance.
All the results are based on thesame D ESERT -P OCKET method in Section 3.2 and are calculated on Set B.
Sampling Space Size
In Figure 11, we evaluate the performance of D ESERT with respect tosampling space size, i.e., the total number of generated molecules before post-processing.
The resultsshow: (1) Increasing sampling space size leads to better performance.
With a larger sampling space,
D ESERT finds more molecular shapes complementary to pockets, leading to a performance rise.
The shape can effectively prune the sampling space for screening.
Instead of directly searchingmolecular space, D ESERT achieves a similar performance by pruning the space from 200K to 10Kwith molecular shapes.
More Ablation We also do ablation study about the usage of different seed shape .
We refer thereader for more details to the Appendix 2.3.
Conclusions
In this study, we propose a zero-shot drug design method D ESERT , which splits the drug designprocess into two stages: sketching and generating.
D ESERT bridges the two stages with the molecularshape and utilize a large-scale molecular database to reduce the dependence on experimental data anddocking simulation.
Experiments show that D ESERT achieves a new state-of-the-art at a fast speed.
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