Zero-Shot 3D Drug Design by Sketching and Generating
Authors: Siyu Long
Yi Zhou
Xinyu Dai
Hao Zhou
Venue: NeurIPS 2022 Accept (2022)
Keywords: Zero-Shot
3D Drug Design
Pre-training
Generation

Abstract: Drug design is a crucial step in the drug discovery cycle. Recently, various deep learning-based methods design drugs by generating novel molecules from scratch, avoiding traversing large-scale drug libraries. However, they depend on scarce experimental data or time-consuming docking simulation, leading to overfitting issues with limited training data and slow generation speed. In this study, we propose the zero-shot drug design method DESERT (Drug dEsign by SkEtching and geneRaTing). Specifically, DESERT splits the design process into two stages: sketching and generating, and bridges them with the molecular shape. The two-stage fashion enables our method to utilize the large-scale molecular database to reduce the need for experimental data and docking simulation. Experiments show that DESERT achieves a new state-of-the-art at a fast speed.

Zero-Shot 3D Drug Design by Sketching and Generating
Siyu Long"l, Yi Zhou', Xinyu Dai', Hao Zhou
National Key Laboratory for Novel Software Technology, Nanjing University ByyteDance AI Lab
Institute for Al Industry Research (AIR), Tsinghua University
1 Longey@smail. njju. edu. en zhouyi mai haiveebtteaance. com daixinyuênju edu. zhouhaodair. tsinghua. edu. en
Abstract
Drug design is crucial step in the drug discovery cycle. Recently, various deep learning-based methods design drugs by generating novel molecules from scratch, avoiding traversing sarge-scale drug libraries. However, they depend on scarce experimental data or coneuconu docking simulation, leading to overfitting issues with limited training data and slow generation speed. In this study, propose the zero-shot drug design method DESERT (Drug dlEiign by SkErching and geneRa Ring).. Specifically, DESERT splits the design process into stages: sketching and generating, and bridges them with the molecular shape. The two- stage fashion enables our method utilize the large-scale molecular database reduce the need for experimental data and docking simulation. Experiments show that DESERT achieves new state-of-the-arr fast speed
Introduction
Drug design is crucial step in the drug discovery cycle, which is the inventive process of finding new drugs based on a biological target (usually protein pocket) 2 However, seeking appropriate drugs for particular target is quite challenging due the enormous space of drug candidates (almost 1003). Traditional drug design approaches usually employ virtual screening [4 5 6) and molecular dynamics 7 8 to traverse in a large scaled drug library, which is time-consuming and could not produce novel drug candidates. Recently, a line of work proposes to realize drug design by generating drug molecules from scratch using deep generative models [9] 2 10 which is quite promising due to the fast speed and the ability of de-novo drug design.
Most of current drug generation model are developed upon ID (SMILES) 11 12 13 or 2D (molecu- lar graph) 14 15 16 17
18 19 molecular structures, which heavily rely on expensive experimental data for supervised training while ignoring the 3D interaction information between the drug and the pocket. In word, they attempt to find a molecule that maximizes the score given by a bio-activity pre- dictor trained experimental data. They employ different optimization methods, such Generative Adversarial Network (GAN) 20), Bayesian Optimization (BO) (2 22 91, Reinforcement Learning
(RL) 23
24
20
25
26
27 Evolutionary and Genetic Algorithms (E. (EA/GA) [28] 29 30 31 32)
and Markov Chain Monte Carlo (MCMC) 33 34J, in the molecular space for obtaining desired drug molecule under certain constraints. However,
argue that purely data-driven approach of drug design is practically limited, since in most cases, the quantity of experimental drug-pocke pairs hardly enables supervised training of de-novo drug design. Generally, most protein pockets lack bio-activity data, and learning on noisy and deficient data may lead to severe overfitting problems.
'Code available at https://githbb.co http::/githuh..oo/loongoooo www.///ogggoogggnnn/eeeeeeeeeeee
Work was done when Siyu Long was a research intern at Bytedance AI Lab.
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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Average
18.54
binding
After binding
Avtrage
1.52A
Figure I: When a drug is binding to a pocket, its shape does not change much (with RMSD less than 1.391A) and is complemen- tary to the pocket.
Figure 2: DESERT splits the whole drug de- sign process into two stages: sketching the shape and generating the molecules.
Recently, several drug design models have been proposed directly generate drug molecules in the 3D space, the realistic space of drug-target interaction. Generating in such space is very promising for the potential of leveraging some prior knowledge (e.g., physical knowledge) instead of entirely counting on data-drived methodology. Specifically, Masuda et al. [35] and Luo et al. (2) propose efficient 3D generative models to learn the atom density conditioned on protein pockets, with GAN and auto-regressive models, respectively. Nevertheless experimental data are still obligatory in their models for achieving satisfactory results. Even more noteworthy GEKO [10), which combines the ideas of 3D generation and physical simulation to obtain state-of-the-art drug design performance without the help of large-scale experimental data. Intuitively, GEKO performs geometrie editing in 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 not always be accurate enough, especially in some complex settings (40) In such a case, being heavily dependent on the docking accuracy could hurt the generalization of the proposed drug design model.
In this paper, we propose the zero-shot approach DESERT, namely Drug dEsign by SkEtching and RaTing. Motivated by the idea of structure determines properties [42] 43 45), DESERT
built on the assumption that molecular shape determines bio-activity between drug molecules and its target pocket. In other words, we suppose that a drug candidate would have satisfactory bio-activity to target pocket if their shapes are complementary (see Figure II.
With such prior, as shown in Figure 2 DESERT splits the whole drug design process into two stage: sketching and generating, which employs the molecular shape as the bridge of the two stages. Such splitting makes DESERT enjoy two advantages: a) DESERT does not heavily rely on docking simulation, which only optionally uses docking for post pest-process and thus avoids the aforementioned disadvantages of GEKO. b) DESERT abandons the expensive experimental data. Specifically, in the sketching stage, we only need to sample some reasonable shapes complementary to the target pocket. In the generating stage, DESERT proposes to employ a generative pre-trained model (from shape to concrete molecular) to fill the shape obtained in the last sketching stage. Notably, the generative pre-trained model is only trained on the ZINC database, which contains 1000M pairs of molecules and their corresponding shapes. This process does not rely on experimental data, making DESERT work in a zero-shot fashion.
Note that DESERT is not baseless. Besides the idea that structure determines properties (an important concept of Structural Biochemistry). also have preliminary results verify our assumption. Figure shows that after binding to pocket, the root-mean -quare deviation (RMSD) is not too shabby (1.391Å) compared with molecular conformation generation methods such as CGCF [46) (1.248A) and ETKDG [47] (1.042A). The results suggest that the molecular shape is stable when molecules bind proteins. We also show in Figure T that ligand often attaches tight to pocket, which means their shape is complementary.
In summary, contributions three-fold: (1) We propose DESERT, generative method for de-novo 3D shape-base based drug design in the zero-shot setting. (2) DESERT trains a pre-trained model
2 "The generation stage of DESERT can also be equipped with chemistry priors, we conduct some experiments in Appendix 2.2.

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Training
Designing (Ligand-based)
Designing (Pocket-based)
Reconstrvcted Nokcuks
Shapas
Shaps2Mol
Shape2Mol
Generating
Shape2Mol
Generating
1000M
Notecuta
Molicula
(a) Pre-training with massive un- bound molecules
(b) DESERT with ligand-based sketching
(c) DESERT with pocket-based sketching
Figure 3: Overview of DESERT. In Figure (a), we use massive unbound molecules to train the SHAPEEMML. which includes sketching variants. In Figure (b) and (c), use the SHAPEEMOL to generate 3D molecules to fill in the given shapes. In Figure (b) where exist- ing drugs are available, treat their shapes desired molecule shapes (ligand-based), In Figure (c) where only protein pockets are given, we heuristically sample reasonable molecular shapes from the pockets (pocket-basedd)
from massive unbound molecules eliminating the constraints labeled data. (3) DESERT achieves new state-of-the fi-the-art art result (the docking score improves 0.79kcal| /ml and 2. 2.93kcal/ mol over the best supervised method on two datasets) at a fast speed (about 20 times faster than GEKC
2 Proposed Method: DESERT
In this section, we describe the proposed method detail. Inspired by the two aforementioned preliminary studies (see Figure I and Figure ( DESERT designs drugs for proteins in two-stage fashion and employs the shape as a bridge since previous work has shown the feasibility of designing
drug by molecular shape [49 50, 51 52 [33.
Specifically, DESERT designs drugs by first sampling appropriate shapes complementary to the target pocket and then mapping the shapes to specific molecules. In Section 2.1 we first introduce the overall picture how DESERT works zero-shot setting. Then we pose two challenges: how to sketch the reasonable molecular shapes and how to generate corresponding molecules based on the shapes. We put forward solutions Section 2.2 and Section 2.3 respectively.
2.1 Zero-Shot Pipeline
In this section, focus on how DESERT designs drugs in the zero-shot setting. Briefly speaking, DESERT produces molecules in 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 DESERT needs to sample molecular shapes. In the zero-shot case where no reference protein ligands are available, DESERT samples reasonable shapes from protein pockets (see Figure 3cb based on biological observations. Besides, DESERT can also reuse the shape of ligand design novel one (see Figure 3b). Details listed in Section 2.2
Zero-Shot Generating DESERT generates molecules through a pre-trained generative model, namely SHAPE2MOL, which can convert given shape into diverse molecules (see Figure [a). In this procedure, we utilize massive unbound molecules to train the model. Thus no information about proteins is needed. Details of this model are presented in Section 2.3
2.2 Sketching Molecular Shapes
The sketching stage is responsible for deciding what desired molecules look like. In this section, we show how we design two heuristic methods to sketch the shapes of desired molecules.
} FFor clarity, the following terms will used throughout study: "bound drug/molecule" (or "unbound drug/molecule") refers the drug/molecute that is bound (or unbound) to proteins (48).
We calculate the speed by measuring the time from given pocket getting 100 molecules.
3

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Pocket shape
Seed shape Molecular shape
Figure 4: A 2D illustration sampling molecular shapes from pockets. The 2D molecule in the figure represents potential drug that fits the molecular shape.
There are mainly two cases when we sketch a molecule shape based on whether the ligand is provided. When the ligand is available, the sketching process can be trivial since molecules with similar shapes have similar properties. is reasonable to directly use the ligand's shape as the shape of the desired molecule, which we call Ligand-based Sketching. If the ligand of the pocket is unavailable, the challenge is how to obtain shape that has a high potential to bind to pocket. We name this Pocket- based Sketching, and our main idea sample a region with an appropriate size complementary to the surface of the pocket.
Our idea for Pocket-based Sketching is based two main observations:
1. Ligands mainly lie in the area close the pocket surface. Figure shows that the shape of satisfactory ligand is tightly complementary to the pocket.
2. 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) obtain the desired molecule shape, which is of the appropriate size and complementary to the pocket surface. We achieve this goal by finding another shape (namely seed shape) that intersects with the pocket, where the intersection has similar size to molecule. We also show 2D illustration in Figure
2.3 Generating 3D Molecules by SHAPE2MOL.
In this section, introduce SHAPE2MOL, encoder-decoder network mapping shape diverse and high-quality 3D molecules. There are plenty of unbound molecule data, e.g., 1000M molecules in the ZINC database, making it possible learn large-scale pre-trained generative model from shape to molecule.
Concretely, we formulate the problem voxelized as a 3D image (see [2..T], and the 3D molecule converted to a sequence (see 2.3.2.. Our generative approach is capable of modeling any complicated molecule structure and the linarization makes large-scale pre-training easier to implement.
image-to-sequence generation, where the shape is
2.3.1 Encoder: Voxelized Shape
The shape encoder is 3D extension of the VIT (56). where 3D patches instead of 2D patches in the original VIT. Let A denote the set of atoms, a molecule m can be constructed as collection of atoms and their corresponding coordinates:
m {(a, c)la E A, ER)
Given molecule m, we transform its shape into 3D image with a voxelization function Um 3 {0, 1):
Um (x. = 1 3(a c) e m, (2, y, =) - cll2 S r(a) +
otherwise
where denotes the Van der Waals radii [57]. perturbed noise which helps prevent overfitting
58
"The pocket can be generated by CAVITY 54 or f- f-pocket 55 In this study, all protein pockets are generated by CAVITY.

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[B08]
Shape Encoder
3D Molecule Decoder
[B08]
Figure 5: The architecture of SHAPE2MOL.
Te
T
[BOS], T., T [BOB], Ty- (BOB)
[BOB), T,, T, (EOBB) T, T,, T, (FOS]
Tokenization
Linearization
T,
Molecule
Tree Structure
Fragment Sequence
Figure 6: The process of converting molecule into sequence.
2.3.2 Decoder: Linearized Molecule
The molecule decoder our model 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 ID sequence. To address this, we propose 3D molecule decoder, which handles a 3D molecule as a sequence of tuples. The sequence object eases the implementation of pre-trained model. To obtain the object, we first cut a molecule into pieces, then convert to a sequence.
Tokenization We cut molecule into pieces so that the generative process can be easily factorized. Our principles are three folds: preserving the functional groups since they are vital for determining molecule properties [60). (ii) avoiding too large size of the vocabulary ease the pre-training process (6T), (iii) no circles exist in the segmented molecules since tree structure is simpler handle than graph. Our method is simple yet efficient: first tokenizing the molecules with BRCIS [62) and then cutting all single bonds attached to a ring. More details about some pilot experiments can found in Appendix 1.2.
Lincarization For the network output, propose utilize linearized sequence represent the target molecule graph, which is not only convenient for training, but also has the strong power to represent any complicated tree structure. We first select an fragment whose degree is 1 as the root of the tree (c.g., T1. T3, T5, and T8 in Figure 6). Then we traverse the tree in the depth-first-travent style(63 63) Whenever we enter leave a branch, we will add two special symbols [BOB] and [EOB] (heginning/ending of a branch), respectively.
More particularly, we tuple (C, P. R) represent fragment F, where C 1 indicator function (64) denoting its index in the vocabulary, P E IR is the translation vector [65]. R e R the rotation quaternion (66). In order stabilize the training process, further discretize the continuous variable and π into pe and RS, respectively. Taking the translation vector as an example, we convert into a binary vector ps, which satisfies:
{
=
P
otherwisc
where L is the max translation length, b is the bin size.
5

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2.4 Training & Decoding
Training Given the output probabilities of the model Pr, RT), which denote the probability of a fragment, a discreterized translation vector, and a discreterized rotation vector, respectively. We calculate the corresponding cross-entropy loss and use their sum as the final loss function.
L -=
Σ
log + P log Pe + R log R
(I)
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 stacked Transformer layers. When training SHAPE2MOL,
set the dropout rate 0.1, batch size 2048, train step 300K and use AdamW [67] with learning rate 5e-4, weight decay Ic-2, and warmup step 4000 as the optimizer. The model developed by ParaGen (68] and trained on 32 Telsa V100 GPU cards for 2 weeks. Following 35}, we also randomly rotate and translate the input shape for invariance.
Decoding We design a decoding strategy to provide diverse and high-quality candidate molecules for a given protein. To achieve diversity, we employ the sampling method Nucleus (69 to generate multiple fragment sequences. Then we convert the sequences back to molecules with greedy algorithm (see Appendix 1.2), which connects the fragments by greedily enumerating the nearest pair of breakpoints. Finally, we do some pest-processing operation to further improve the diversity and quality. We remove the duplicate molecules and leverage the docking simulation to drop molecules that do not pass the affinity threshold.
When testing SHAPE2MOL, we set the threshold of Nucleus sampling to 0.95. For each protein pocket, we sketch 200 shapes. For each shape, we generate 1000 molecules. More details of SHAPE2MOL can be found in the Appendix 1.3.
Results and Discussions
3.1 Experiments
Data 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 is a combination of the test data used in Masuda et al. [35] and Jin et al. (23). Among the proteins, only JNK3 and GSK33 (PDB IDs: 3F12, 4J71) have sufficient labeled data train well-performing bioactivity predictors. We denote the set of these two proteins as Set B and the rest as Set A. Because 1D/2D methods need bioactivity predictors to design drugs for given proteins, we only evaluate them on Set B while evaluating 3D-based methods on both sets.
Baselines We compare DESERT with some baselines for drug design. Based the resource needed for designing drugs, we further divide these baselines into three groups: Guided methods need docking simulation extra bioactivity predictors provide supervision signals. Supervised methods rely on labeled data to train their models. Retrieved methods directly search the database for desired molecules.
Evaluation Following Yang et al. 10 we evaluate the performance of methods from two aspects. (1) the molecular space covered by designed results. (2) the capacity to provide highly active molecules. As a high-quality molecular space should contain diverse, novel molecules with pharmaceutical potential, we use five metrics evaluate the molecular space: Uniqueness (Uniq), Novelty (Nov), Diversity (Div), Success rate (Suce), and Product (Prod). To evaluate the ability to provide highly active molecules, we compare the distributions of Vinaseore 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. For each protein, every method needs to generate 100 molecules for comparison. For 3D methods,
"pnps///iitbb.combbyyeee/e//// cow/byteeancc//Paaaaaaa
Whenever we cut chemical bond in tokenizing, mark the atoms of the chemical bond as breakpoints.

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Table 1: Performance comparison among drug design methods. indicates lower is better.
indicates higher is better.
Uniq (%)f
Succ (%)T
Nov (%6f
Div
Prod
Median (kcal/mol))
Targets
Method
Guided
GEKO
10
100.0
55.7
100.0
0.912
0.51
-9.58
liGAN 35
100.0
0.4
100.0
0.924
0.00
-5.84
Supervised
Set
3D SBDD
69.7
13.6
98.9
0.839
0.08
-8.83
SCREEN (1K)
100.0
25.6
100.0
0.892
0.23
-7.46
Retrieved
SCREEN (200K)
100.0
64.0
100.0
0.889
0.57
-8.66
DESERT-LIGAND
100.0
65.3
87.0
0.786
0.41
-8.89
Ours
DESERT-POCKER
100.0
61.1
100.0
0.908
0.57
-9.62
JT-VAE 9
100.0
13.0
100.0
0.907
0.12
-8.35
RationaleRI
100.0
27.0
35.0
0.884
0.08
-7.75
GA D
30
39.0
24.0
87.0
0.852
0.06
-7.22
GraphAF
26
97.0
0.5
100.0
0.946
0.00
-4.22
Guided
MolDQN
27
76.5
0.0
100.0
0.742
0.00
-5.52
MolEvol
99.5
40.5
63.5
0.742
0.17
-8.19
Set
MARS
33
86.0
31.5
93.0
0,805
0.22
-7.68
GEKO
TU
100.0
57.0
100.0
0.910
0.52
-9.19
liGAN 35
99.8
0.2
100.0
0.923
0.00
-5.34
Supervised
3D SBDD
99.9
5.2
100.0
0,853
0.05
-6.39
SCREEN (IK)
100.0
3.0
100.0
0.891
0.03
-6.94
Retricved
SCREEN (200K)
100.0
32.0
100.0
0.882
0.28
-7.95
DESERT-LIGANE
100.0
18.0
100.0
0.913
0.17
-7.34
Ours
DESERT-POCKER
100.0
61.0
100.0
0.907
0.55
-9.32
further the local minimization module in Vina [36] optimize the generated structures. For ID/2D methods, we first use RDKit to generate the 3D conformer of their results. For method SCREEN, we sample IK and 200K molecules from ZINC and return 100 molecules with the highest Vinagcore as the generated results.
3.2 Main Results
Table shows the results of DESERT and baselines. All values are averaged over target proteins. Our main findings are listed follows:
L.eee zero-shot DESERT achieves the SOTA result at a fast speed. DESERT'S performance strong compared with GEKO, an MCMC-based model with huge sampling space. Compared with GEKO, which works in a tria--and-rrror way, DESERT makes a more clever choice by pruning the space with its biological knowledge regarding the shape and quickly finds good solution with limited hints from a teacher. We compare the generation speed in Figure
II. The shape helps DESERT produce high quality molecules. The molecular space of SCREEN the ZINC database, while that of DESERT is generated by the pre-trained model SHAPE2MOL. which is aware of the pocket's shape. According to the table, both DESERT-LIGANU and DESERT- POCKET show observably better performance than their counterparts, i... SCREEN (IK) and SCREEN (200K).
III. More comprehensive exploration of protein pockets benefits performance. Instead of using the molecular shapes of reference ligands as input, DESERT-POCKET comprehensively explores the protein pockets by sampling multiple molecular shapes from them. Therefore, has the potential to obtain diverse, high-quality molecules that bind to protein pockets in different regions. While DESERT- LIGAND only considers one region, i.e., the region that ligands lie in, limiting the exploration of protein pockets. We show case in Figure g
7

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60-
MoDOk
20-
S00k
GAID
20
DESERT-LIGAND
DESERT-POCKET
[Modian]
Figure 7: The absolute value of the median vina score and speed different methods on target protein 3F12.
Figure 8: Molecules generated by DESERT on protein 4CG9.
Table 2:
Comparison of shape faithful- ness and structure rationality. "Random" the Shape Tanimoto between two random molecules, "Real" is the Free Energy ref- erence ligands.
Reference(2RDG)
IIGANILigand)
DESERT-LIGAND
Free Energy (kcal/mol)
Method
Shape Tanimoto
Random Real IiGAN (Ligand)
0.325
Figure 9: cases from liGAN and DESERT.
167.28
0.869
289.55
DESERT- DESERT-LIGAND
0.875
188.54
IV. Unsupervised methods have larger potential than the supervised counterparts. As labeled data inadequate, e.g., scPDB only has 16,034 entries, the supervised methods easily collapse to the main molecule pattern in their dataset. When generating molecules with them, we often get the same molecules, e.g., 3D SBDD can only generate 16 unique molecules for protein 4OQ3. DESERT utilizes massive unbound molecules, which leads the learned space being denser. Combing with an appropriate 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 previous shape-based models. In Table 2
Shape Tanimoto evaluate the faithfulness and Free Energy to quantify the rationality [74). We compares DESERT-LIGAND with liGAN (Ligand), variant liGAN (35) which utilizes the existing ligands. Although liGAN (Ligand) achieves high performance on Shape Tanimoto, its atom- based decoding strategy does not guarantee the correct relative position between atoms. Therefore, liGAN shows higher Free Energy, indicating that unrealistic structures may appear. Figure shows case where liGAN produces an ill ring structure.
3.4 Ablation Study of Generating
In this section, we evaluate several designs of the generating stage in our DESERT method, which relate the pre-trained model and decoding strategy. All results based on an extra test from ZINC and a smaller version of SHAPE2MML. 10
Pre-training Configuration We evaluate the model quality different pre-training configurations (mainly focusing on the size the model and training data). The results in Figure show: (1) Larger model achieves better performance. Devlin al. (75) observes similar phenomenons in natural language modeling. (2) Performance saturation occurs when the dataset is of moderate size. As the
scPDB is a hiihh-uualit labeled dataset for 3D drug design. 'Shape Tanimoto 71
72 73 measures the similarity between the input and generated molecules.
Shape Tanimoto(A, B)
AUB where A and are two molecular shapes.
10e extra test set contains 10K molecules. The smaller SHAPE2MOL has 512 model dimension and 6 layers of encoder and decoder. We greedy decoding strategy and remove post-processing.

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0.956
0.955
0.929
10.0
9.5
-0.6
0.965
0763
0.77
[Medien] (icalima))
GESERSACOSS1
00.5
-04
8.0
00.3
7.0
DESASTUIAADO
301001200K
0.2
MAR3
0.1
DESERTLIAAAD
859
3.833
0.74
0.75
100
200
100
200
1000
molecules)
4.5M
102M
Figure 11: Comparison of sampling space size.
Figure 10: Comparison of different pre- training configurations.
Table 3: Performance comparison incomplete protein pockets, we recover the complete pockets by aligning the incomplete pockets to the structures from PDB database. We managed to recover 55 pockets and apply them. For 3D SBDD, we use the released code and apply the same post-processing used in DESERT to it.
protein targets. Because the original test data only contains
to
Metric Med.
3D SBDD w/o post processing
3D SBDD W post-proseeee post-processing
PESSERT-PCCKT w/o post pooscsscessiigg
DESERT-POCKET W post-processing
Vina Score (kcal/mol)
-6.069
-7.584
-6.148
-9.410
QED
0,522
0,501
0.614
0,549
0.672
0.623
0.612
0.616
Diversity 0.873
0.826
0.926
0,908
map from radii to atoms is easy to learn, the model can capture it with moderate dataset. Liu et al. 76 reports a similar result that large dataset does not necessarily lead to better quality.
More Ablation We also do ablation study about some model variants (including discretization and robust training) and decoding strategies. We further study chemical information driven design and atom-based pre-traine pre-training. We refer the reader for more details to the Appendix 2.2.
3.5 Ablation Study of Sketching
In this section, study the sketching stage in our DESERT method, which includes the effect of sampling space size and seed shape on the method's performance. All the results are based on same DEEERTTPPCKKE method in Section 3.2 and are calculated on Set B.
Figure I1 we evaluate the performance of DESERT with respect to sampling space size. i.c., the total number of generated molecules before post-processing. The results show: (1) Increasing sampling space size leads better performance. With larger sampling space, DESERT finds more molecular shapes complementary to pockets, leading to performance rise. (2) The shape can effectively prune the sampling space for screening. Instead of directly searching molecular space, DESERT achieves similar performance by pruning the space from 200K to 10K with molecular shapes.
Sampling Space Size
More A Alation
We also do ablation study about the usage of different seed shape. We refer the reader for more details the Appendix 2.3.
3.6 Apply DESERT to More Protein Targets
To test the generalization ability of our method more widely, we also apply DESERT to the test data from Luo et al. (2). which contains 100 protein targets. As shown in Table based on the idea of

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structure determines properties, DESERT-POCKER generalizes well on different target proteins in both setting, i.e., with/without post-processing. Supervised methods, like 3D SBDD, hindered by scarce training data, can not generate diverse molecules. In contrast, training on massive unbound and drug-like molecules, DESERT easily generates diverse and promising molecules. Moreover, sketching molecular shapes based on given pockets also contributes to the better binding affinity of DESERT. However, DESERT gives a lower SA score than 3D SBDD. We assume that it is because the generated molecules of DESERT tend to be structurally complicated, which leads to slightly worse synthesis score.
Conclusions
In this study, propose zero-shot drug design method DESERT, which splits the drug design process into two stages: sketching and generating. DESERT bridges the two stages with the molecular shape and utilize a large-scale molecular database to reduce the dependence on experimental data and docking simulation. Experiments show that DESERT achieves a new state-of-the-ard at a fast speed.
Acknowledgements
We would like to thank the anonymous reviewers for their insightful comments. Both Hao Zhou and Xinyu Dai are the corresponding authors. This work is jointly supported by Guoqiang Research Institute General Project, Tsinghua University (No. 2021GQG1012) and National Science Foundation of China (No. 61936012 and 619761 14).
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Checklist
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(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
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(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes]
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14


Reviews:
[{"review_id": "VVC9g0n2Ylt", "reviewer": "Reviewer_M9mc", "summary": "This paper proposes a 3D molecular generative model DESERT for structure-based drug design in a zero-shot manner. The method involves two stages: first sketching the molecular shape in the protein pocket, then generating molecules based on the shape. Based on the assumption of \"structure determines properties\", the method aims to find molecules whose shapes are complementary to the pocket and thus massive unbounded molecular data (e.g. ZINC)  can be utilized to train the shape2mol model.", "questions": "- I'm a bit confused about the sketching part (Sec. 2.2). How do you get the seed shape? In algorithm 1 in appendix, how is the volume threshold t and step size alpha determined?\n\n- In Sec. 2.3, what is the output of shape encoder and the input of shape decoder? How is the spatial correspondency established in the proposed network architecture?  Is there any guarantee that the generated molecule will satisfy the shape constraint?\n- Which resolution is the shape voxelized at (Sec. 2.3.1)?  Since the number of voxels increases cubically to the pocket size, will it have the scalability issue?\n- In line 167, the authors stated \"For each protein pocket, we sketch 200 shapes. For each shape, we generate 1000 molecules\". In line 191, it seems all other baselines methods \"generate 100 molecules for comparison\".  I'm wondering how the samples generated by the proposed method (200 x 1000) are utilized to compute scores in Table 1 for a fair comparison with other existing methods? Also, \"20 times faster than GEKO\" refers to a per-pocket or per-sample inference time?\n- Although the proposed method is based on the assumption that \"structure determines properties\", I'm still curious about how the generated molecules could have so good Vina scores without any protein pocket information leveraged in the generation process.", "limitations": "The authors discuss some limitations in the experiment section. There is not negative social impact", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The main idea of drug design by sketching and generating is novel and well-motivated\n- The proposed method shows better performance over existing methods in the experiments\n- The analysis in experiments is comprehensive\n\n\nWeaknesses:\n- Don't have a related work section discussing the connection between proposed Shape2Mol and existing shape-based molecular generation methods (Ref. [49] - [53])\n- The proposed method section is not very clear to me. See details in Questions", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "MM1zPWNNZs", "reviewer": "Reviewer_Qdyd", "summary": "This paper presents a new framework and pre-training scheme for designing pocket-conditioned ligands. Specifically, it leverages the shape (voxel) of ligands and protein pockets in pre-training a sketching network that specifies the shape of the protein pocket, and learning a shape2Mol network that predicts specific ligands that fit into the given voxel shapes. The model were trained over billions of molecules in ZINC database and achieved state-of-the-art results.", "questions": "See above, I put questions related to each point in the strengths and weaknesses part.", "limitations": "The limitations are not discussed in the paper but may come from two sides: (1) whether this method can generate diverse ligands that bind to protein targets, (2) the efficiency of the method. The first one concerns whether the formulation makes sense in the biological context, or if not, is still a good way to leverage unlabeled data. The second one needs a bit more experiments to support.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength\n* This paper proposes an interesting idea to recognize the voxel/shape of the protein pocket and leverage the information for pre-training. It is quite different from the other line of work which leverages the atoms of both proteins and ligands that aim to learn the interaction for generative design. It is interesting to see how this idea plays out and I would suggest adding more discussions about how it is considered in traditional structure-based drug design, shape/voxel vs. atomic interaction.\n* The proposed idea is proper in real-world scenario, where the paired ligand and pocket data are limited, thus I am convinced pre-training is needed. \n\nWeakness\n* How is sampling achieved for generating diverse molecules for a specific pocket? In the paper, only part mentioning it refers to Nucleus [68], does it imply that sampling is only involved after generating molecule from the shape? \n* Efficiency is neither compared nor discussed. The authors make a fair argument in the introduction about the drawback of GEKO, which performs equally powerful as the proposed method. Given the proposed method also takes tons of training time, it would be good to see the comparison and discussion.\n* Many discussions or relations to the literature are not discussed. e.g. how tokenization and lineraization are related to the literature of graph generation, framgment/scaffold-based molecule generation, etc. \n* Experiments are only done over 12 protein targets, I would suggest adding more experiments, e.g. 100 targets in 3D SBDD paper.\n* Ablation study is not complete, the comparison method, especially deep learning-based SBDD, 3D SBDD and liGAN are based on atomic reconstructions, it would be interesting to see whether the proposed model benefit from fragment-based method or the new training framework.\n* Many details are missing or less mentioned,  For example, how the post-processing is done. How many bins are cut for the rotation and translation operations, where the origin is, how the tranformation is done, etc. In training & decoding section 2.4, only ligands data are mentioned while no protein data are mentioned. Figure 10 is not clear, not sure what shape, rotation, translation and category mean. \n* Codes are not uploaded, which hinders the reproducibility of this work. It would be beneficial to the community if authors consider publishing the codes if the paper is accepted.\n\nOverall, I think this paper makes a fair point and it is a good attempt in the direction of pocket-conditioned ligand design. I am willing to raise my score if the questions get answered.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "10s0g2G9yiA", "reviewer": "Reviewer_fWm8", "summary": "This paper proposes a zero-shot drug design method based on sketch. Starting from the property that structure determines properties, the paper uses a sketch model of protein pocket shapes and a pre-trained generative model based on molecular shape sketches to achieve zero-shot drug design that does not rely on docking experimental data and docking simulations.", "questions": "1.\tIs there a theoretical basis for using the intersection of a seed shape and a pocket shape to obtain a molecule shape? How are the shape, size, and initial position of the seed shape chosen in the algorithm? Different initial parameter settings will result in very different molecule shapes and thus completely different generated molecules. How do different initial parameters affect the generated results, and is this approach reasonable?\n2.\tThe model is encoded using discretized spatial information, while the generation directly generates the 3D coordinates of the molecular functional groups. Does the model then still generate the same or at least similar molecules when given a molecule shape that has been rotated and translated? This property is necessary for the generation of physical entities.\n3.\tAlthough the pre-trained model uses a larger molecular dataset, not all molecules in the ZINC database are used for pharmaceutical purposes as far as I know. The molecules generated by the pre-training model may not be suitable for use as drugs. Measurements of drug-like properties(QED), ease of synthesis(SA) and lipid solubility indicators(LogP) and comparisons with other models should be included in the experimental section.\n4.\tIt is mentioned in the paper that the pre-trained model uses a large amount of computational resources, therefore the source code and pre-trained model should be made public for easy reproduction.\n", "limitations": "Using only information about the shape of the protein pocket avoids some of the limitations of the data set and binding simulations. However, the model is limited by ignoring information about the different interaction forces (hydrogen bonding, π-Stacking, etc.) that occur when molecules bind to proteins depending on the type of atom since it completely abandoned such information.\n\nDuring the generation phase, the tree-like structure is less expressive for molecules with complex structures. Moreover, the connecting step based entirely on greedy algorithms may lead to unreasonable results.\n", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\nThe paper presents a molecular generation method based only on shape sketching, which is a very novel approach. Also, the generation method without using protein binding data and simulations is enlightening for future work.\n\nWeakness:\n\tAlthough the sketch-based generation idea is novel, the generation model is still similar to the transformer-based sequence generation model used in machine translation tasks. The paper is also not clear enough in the method description, only the design of the encoding and decoding method and the paper citation of the model used are given, the specific model architecture as well as the parameters should be given.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept