Knowledge Guided Geometric Editing for Unsupervised Drug Design
Authors: Yuwei Yang
Siqi Ouyang
Meihua Dang
Mingyue Zheng
Lei Li
Hao Zhou
Venue: ICLR 2022 Submitted (2022)
Keywords: drug discovery
3D molecular generation
monte carlo sampling

Abstract: Deep learning models have been widely used in automatic drug design. Current deep approaches always represent and generate candidate molecules as a 1D string or a 2D graph, which rely on large measurement data from lab experiments for training. However, many disease targets in particular newly discovered ones do not have such data available.  In this paper, we propose \method, which incorporates physicochemical knowledge into deep models, leading to unsupervised drug design. Specifically,  \method directly models drug molecules in the geometric~(3D) space and performs geometric editing with the  knowledge guidance by self-training and simulated annealing in a purely training data free fashion. Our experimental results demonstrate that GEKO outperforms baselines on all 12 targets with and without prior drug-target measurement data.

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KNOWLEDGE GUIDED GEOMETRIC EDITING FOR UN- SUPERVISED DRUG DESIGN
Anonymous authors Paper under double-blind review
ABSTRACT
Deep learning models have been widely used in automatic drug design. Cur- rent deep approaches always represent and generate candidate molecules as a ID string 2D graph, which rely on large measurement data from lab experiments for training. However, many disease targets in particular newly discovered ones do not have such data available. In this paper, we propose GEKO, which incor- porates physicochemical knowledge into deep models, leading to unsupervised drug design. Specifically, GEKO directly models drug molecules in the geomet- ric (3D) space and performs geometric editing with the knowledge guidance by self-training and simulated annealing in a purely training data free fashion. Our experimental results demonstrate that GEKO outperforms baselines on all 12 tar- gets with and without prior drug-targed measurement data.
INTRODUCTION
Finding drug molecule that specific diseases is major and significant challenge in the field of medicine. Traditional drug design methods usually use a screening approach (Bajorath) 2002), such as molecular docking(Ferreira al., [0055) Meng et al., (011). to filter molecules from a large database, which cannot generate novel molecules and heavily relies on expert experience for further modification. recent years, the surge of deep learning has benefited the field of automatic drug design. Intuitively, automatic drug design can be modeled as a conditional generation problem, namely generating ligand (i.e., drug molecular) from a disease-related target (usually protein pocket). Currently, majority of the deep learning-bai -baeed drug design methods express molecules as a SMILES [Weininger] 1988) string (1D) or a graph (2D). which encourages the application of sequence-based (olommz--ombarclll et al. 2018 Kang & Cho. 2018 Segler et al. (018)) and graph- based (Seff et al. | et al. 2018 Ma et al.
2018: Simonovsky & Komodakis 2018 Samanta
al.
2019 De Cao & Kipf 2018) models in drug design.
However, deep neural networks that model molecules in 1D/2D formats requires massive experi- mental activity data to learn relations between low dimensional features and their activities against specific protein targets. This is not only expensive but also limits these models' application scope into proteins with rich activity data. For example, Din al. (2018b) formulate the drug design prob- lem as translating one molecule to another with better properties supervised with labeled molecular pair data. Similarly, Xie al. (202I) and Jiin et al. (2020) use activity predictors trained from target-specific activity data to guide property optimization via Markov chain Monte Carlo and rein- forcement learning respectively.
In fact, we can leverage plysicochemical knowledge to estimate the bio-activitics without any la- beled data given 3D structural information. In general, drug molecule exhibits its activity by forming 3D complex with a disease-related target. The tighter they bind, the more effective is the drug. The tightness can be measured by the minimum binding energy between these two chemical compounds and we have plenty of cxisting molecular docking software to estimate this energy. How- ever, the searching and optimization for the minimum energy is a computational expensive process, which makes it non-trivial efficiently extract the knowledge in docking and apply it automatic drug design.
In this paper, we propose Gometric Editing under KnOwledge guidance (GEKO) achieve unsu- pervised drug design. We utilize the knowledge from molecular docking, drug-likened and synthe- sizability these knowledge is broadly applicable for all targets and offers great resources for guid-

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ing drug design. Inspired by the knowledge form and a classical medicinal concept called structure activity relation (Guha 2013 we aim to learn the effects of incremental molecular modifications in 3D molecular space. In more details, we carefully design geometric molecular editing module to traverse the space by adding or deleting fragments and modeling the dihedral angles in between. Then hierarchical message passing neural network is tailored capture both atom and fragment level information. We train the network with self-generated data and use simulated annealing to generate molecules under the guidance of knowledge. With a large amount of self-generated data, the model is able to learn the fundamental knowledge for drug design more efficiently and eliminate the dependence on experimental labeled data.
We compare our method with baseline models on 12 targets including those without sufficient ex- perimental data for developing data-driven models. GEKO outperforms other methods on both the 3D molecular space exploration power and capability of generating highly active molecules. The generated 3D molecules show good shape complimentarity with the target surface, which indicates strong interactions between the binding partners.
2 RELATED WORKS
3D Drug Design Generating 3D molecules is a relatively new area in Al powered drug discovery due to its intrinsic difficulty. Within this field, 3D drug design is a non-trivial task which requires the model to generate new 3D molecules geometrically constrained by the target binding site and also achieve multiple optimized drug-like properties. Comparing to molecular generation ID/2D representation, the additional dimension significantly increases the explorable molecular space. Re- gardless of its significance, limited efforts have been made in this field. |Masuda et al., (2020) use a variational autoencoder ( (VAE) to learn the 3D molecular distribution conditioned on target struc- ture using DrassDockedee
(Francoeur et al., 2020). 3D ligand-target complex database. Due to the limited amount of experimental measured complex data, many 3DD complexes in this dataset are generated using molecular docking. Despite of the large amount of training data, the model still suffers from poor binding affinities of the generated molecules compared to an experimental refer- ence. However, our approach, guided by structural knowledge, can successfully generate molecules with high binding affinity while keeping many other drug-relater properties.
3D Conformation Prediction Within the scope of 3D molecular generation, 3D conformation pre- diction is a better studied field comparing 3D drug design. This task is defined as predicting the 3D structure of a molecule with a given 2D molecular graph, which defines the number of atoms and the connectivity between atoms. These methods can largely be divided into two groups by the way they formulate the 3D generation problem. The first class aims to directly generate the atomic positions or the inter-atomic distance matrix (Mansimov et al., 2019 Simm & Hernandez- 2020 Xu et al. 1 Shi et al. 2021). Methods in this class demonstrate initial success on relative small molecules, such as those in GEOM-OM9 dataset (Axelrod & Homez-Bombarelll 2020). How- ever, their performances drop significantly when the molecular size increases, more specifically on drug-like molecules.
The other class predicts the flexible dihedral angles which is the major cause of conformational flexibility in 3D molecules (Gogineni et al. 2020 Ganea et al., 2021). Modeling dihedral angles shows more stable performance when the number of rotatable bonds increases (Ganea et al. 202 Unlike predicting the distance metrics, which tends to overparameterize the degree of freedoms in the molecules, dihedral angles represent the molecular flexibility more straight forward. This kind of approach has been widely used in even larger molecular conformation prediction, namely the proteins. In this work, we integrate this idea into our 3D geometric editing procedure.
1D and 2D Drug Design Deep learning methods have been applied to facilitate drug design mainly on the 1D and 2D representations. Drug design is essentially optimization task, and previously deep models have used various approaches to optimize the properties, such as Bayesian optimization in latent space (Gömez-Bombarelli et al. | 2018 J et al. 2018a] Winter et al. 22019) reinforcement learning (De Cao & Kipi 2018 Popova et al.) 2018 You et al. 2018 Popova et al. 2019 Shi et al.)
'The structure of the drug influences its biological activity. Grasping this relation is the fundamental ap- proach for modifying molecular structure and achieving higher drug activities.
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2020
evolutionary and genetic algorithms (Ahn al. 2020 Jensen 2019
Devi et al.
2015
Nigam
ct al.
2020 A Nicolaou et al. 2012) and etc.
More recently, sampling based approach has been used in molecular generation (Fu et al. 2021 Xie
ct al. 202T). Among them, MARS (Xic et al. (022)) has demonstrated statc-of-the- performance in multi-obeective optimization via adaptive Markov chain Monte Carlo sampling. Although this model learns from self- seff-geneatte data, still relies on a data-driven predictor for biological activity optimization, which limits it application scope. In addition, directly transferring low dimensional drug design methods to 3D generation is not trivial, as the 3D molecular space is much more complex due to the rotation of dihedral angles.
THE PROPOSED METHOD
In this section, we address the procedure of GEKO. We first introduce the physical and chemical knowledge used for drug design and the need of 3D geometric editing. Then we describe how to model the automatic drug design as a geometric editing process and its parameterization. Finally, show how to guide the editing process with these knowledge.
3.1 INTEGRATING PHYSICOCHEMICAL KNOWLEDGE IN MOLECULAR GENERATION
We formulate drug design generative process with the objective of finding molecules possessing strong bio-activity with respect to the target protein, together with sufficient level of drug- diug-likeness and synthetic accessibility.
The bio-activity largely depends on the tightness of formed target-ligand complex, which can be measured by the minimum binding energy between two chemical compounds. particular, GEKO proposes to Autodock Vina (Toot & Olson| 2010 Eberhardt et al., [021] (or Vina for short), one of the state-of-the-art molecular docking softwares, to estimate this energy.
Drug-likeness is measured by QED (Bickerton et al.) 2012), which combines molecular weight, number of hydrogen bond donors, molecular surface area and many other drug-like properties. Syn- thetic accessibility is quantified by SAscore (Ertl & Schuffenhauer, (009) based on the fragment composition and molecular complexity Combining three metrics, we then have the objective func- tion for molecule
I (xx) VINAmin(z) log (ED(x) + B log SASCORE(Z).
Though QED and SAscore can be efficiently computed with the 2D structure of molecule, need extra design to make Vina feasible to use. Specifically, Vina estimates the minimum binding energy by stochastical global search in 3D conformational space together with local energy optimization (the calculated energy as Vinajock), which is time-consuming and cannot provide enough estimations in reasonable amount time.
In fact, we can get rid of the global search of Vina, which makes it 100x faster, by directly generating in 3D molecular space. The model can learn generate desired 3D poses that local energy minimization (the calculated energy Vinamin) is adequate for energy estimation. Details will be addressed in the following subsections.
3.2 TRAVERSING THE 3D MOLECULAR SPACE BY GEOMETRIC EDITING
A molecule's 3D structure can be represented as a composition of rigid 3D fragments linked by bonds with flexible dihedral angles. These angles are formed between two intersecting planes, and in chemistry it is defined using four consecutive atoms and related to the rotation of one bond as shown in Figure top left. They the major cause of conformational flexibility in 3D molecular structures. Inspired by this, we propose to first construct a rigid fragment library, and then edit molecules by either selecting fragment in the library add and determining the dihedral angle between the added fragment and the original molecule, or selecting a rigid fragment in the molecule delete.
Note that re-scale the SAscore to [0, 17.
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Dihedral Angle
Fragment Library
Editable bonds
Add
Figure 1: Geometric Editing for 3D Molecular Generation
Rigid Fragment Library Single bond rotation is the major cause of dihedral angle changes, as single bond less constrained and has lower rotational energy barrier comparing to double bonds construct rigid fragment library by breaking single
and bonds in ring structures. Therefore, bonds in 2D molecules from the ChEMBL database (Gaulton et al.) (017), which is a manually cu- rated database of bioactive molecules with drug-like properties. As shown in Figure topp right, the broken bond for generating fragments are labeled as an editable site for molecular construction and hydrogen atom is added maintain the original valency The same 2D fragment with different editable sites are merged as one member in the library and the editable sites are combined. The 3D conformation of the resulted fragments are generated using RDKit (Landrum) 2021). As rota- tional flexibility is constrained inside fragment, each 2D fragment only corresponds to one or a few valid 3D conformations and each conformation considered as separate member in the fragment library.
Using rigid fragments as building blocks can enhance the validity of generated molecular structures and largely reduce the space of possible conformations by taking advantage of existing knowledge.
Operations of Geometric Editing GEKO places an initial seed molecule within a target binding site, and builds 3D molecular structures by adding or deleting rigid fragments iteratively as shown Figure bootom panel. order achieve adding and deleting operations separately. the editable sites are categorized as addable sites, which correspond to the editable sites with hydrogen atoms attached, and deletable sites which correspond to the the ones with non-hydrogen atoms attached. In adding operations, the model firstly selects an addable bond in the original molecule. Then it chooses a 3D fragment from the fragment library and an addable bond in the fragment to attach to the original molecule. Lastly, it decides the dihedral angle between the two connected components. The hydrogen atoms the selected addable bonds are removed resulting in two partial bonds, which then connected and rotated to the predicted dihedral angle. In deleting operations, the model chooses an deletable bond to break. A hydrogen atom is added to the broken bond to maintain the correct valency.
To better model the fragment-based molecular structure, propose to hierarchical message passing neural networks (HMPNNs) to predict the probability distributions of editing operations mentioned above. HMPNN a two-level mes- sage passing neural network (Gilmer al. 2017) that captures the interactions of both atoms and fragments. It takes a molecule v as input, which represented as a graph = (A, fodee fodge with A the adjacency matrix, gnode and podge as feature vectors. We pass the graph and feature vectors through the first MPNN to obtain atom-level representations:
Parameterization with Hierarchical MPNNs
h
node
MPNN, (g),, d
(2)
IIf fragment only contains atom, is fused with its connecting fragment.
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Original Molecule
Herarchical MPNN
Editngg Preditors
Generateed Moleculle
Molecular Dodking
Atom
Fragmet Level
Drug-lisnness
Synthesizabilyy
Knowlaga Guidance
Figure 2: Proposal Architecture for Editing Operation Prediction.
where hadee is the hidden representation node u. Note that atoms separated by rotatable single bonds are considered as belonging to different fragments. We then regard each fragment as single node, which induces a new fragment-level adjacency matrix A', and obtain the fragment embedding muml by aggregating features of atoms that belong to it using mean pooling, We preserve those edges between fragments with feature vectors zdge initialized by fragment embeddings.
node
MEANPOOLWEV, (N(mal)) E IR
(3)
A CONCAT(Z)) node, node) + Rd
(4)
J.k
2oods where Vi is the set of atoms in fragment The new graph g' = (A', 20ods 2008) is then passed to another MPNN to obtain fragment-level representations.
0 node MPNN2(g') ERd
(5)
odge j.k A₂ CONCAT(O ortde) + b2 E Rdd
(6)
With HMPNN as a representation extractor, we can predict the editing operations mentioned before. Let Fese be the skeleton molecule to be edited. The edge to edit is determined as follows:
h node
mode
edge
HMPNN (Iaae
score MLP,
adge E
(8)
j.k
Paid (r [ssee)
= softmaax(sscorej, (GAA)EE.)
(9)
Pielote (7 [sket) = (oftmaxx(ssorr),))basss
(10)
where E. and Eg are disjoint sets of editable edges on Jssel for addition and deletion respectively. During generation, we sample a directed edge (uggel: Uskel) from 1 + Pateeee For deletion, just replace the deleted fragment Uskel side with hydrogen. For addition, predict the fragment Tirng to add also on Uskel side:
score (L odgo
IR
(11)
Pfragment ( (Iirng [Tkkel T) softmax (score Fmme
(12)
where H is the rigid library. Then we sample a fragment Firag from Piragment The attachable bond on the fragment is then determined jointly by Takel and Tfrng:
frag mode
Frag-node frag edge
HMPNN (Firay)
(13)
node
MEANPOOL(O node)
(14)
score j. MLP:)CONCAAT( o0
frag
frag-edge)) E R
(15)
Pattach Fakel T, Ftrag] softmax (score,, (
(16)
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where E is the set of attachable bonds on fragment Zing. Then we sample a bond (Mlrrgg Vingg) to attach. Finally, we gather the features of four atoms, bond and determine the dihedral angle:
Oskel,
Wskels
Vfrags
Whngg around the new
h angle = CONCAT h maaaa
h frag-node nodee
(17)
Whas
score angle MLP, angle E R
(18)
Pangle (o Pftag a) = softmax (score
angle joEA)
(19)
and sample an angle a from Pangle- We then concatenate the skeleton molecule and fragment molecule together by this dihedral angle a.
3.3 EDITING UNDER KNOWLEDGE GUIDANCE
We use adaptive self-learning to train the prediction network and simulated annealing to sample molecules under the guidance of physiochemical knowledge.
Adaptive Self-Learning A dataset D is collected on-the-fly with the editing process. Denote and x to be the molecule before and after edit respectively. the objective score of x' is higher than x, molecule pair (xr, is added D. Direct maximum likelihood estimation (MLE) training self-generated dataset D provides limited supervision signal. Instead, we train our proposal using weighted maximum likelihood estimation (WMLE) as follows.
arg max
Σ (x,z")ED
X(a' 2x) (o
(20)
6
D
where qo the network and A(z', x)) a monotonic function indicating the score difference between x' and z. Here we choose A(x', x) = min ((()))J((r) WMLE is expected to offer less biased gradient estimation with respect the objective funetion.
Simulated Annealing We use simulated annealing (Laarhoven Aarts
1987) to approximate the global optimum of the given objective function J (x) in 3D molecular space X. Starting from ini-
tial molecule zo, we use the prediction network to propose some neighboring molecule x' of the cur-
rent one 2, each time step f. The proposed molecule can either be accepted .t-1 = x or rejected
Tt-1 xt as determined by an acceptance probability A(z', = min (i, exp( I(a)) }},
where T is the annealing temperature controlling how greedy the process is.
RESULTS AND DISCUSSIONS
4.1 EXPERIMENTS
We evaluate our method on drug design tasks using two sets of biological targets:
Target Set A: diverse set of 10 biological targets with PDB IDs of IFKG, 2RD6. 3H7W, 3POP, 3VRJ, 4CG9, 40Q3, 4PS7, 5E19 and 5MKU used by Masuda et al. (2020). Tar- gets in this set do not have available data-driven activity predictors. Hence, 2D molecule generation methods cannot be applied to these cases.
Target Set B: of 2 targets, namely JNK3 and GSK33 (PDB IDs: 3F12 and 4J71) Well-performing ligand-based scoring functions have been developed for these targets us- ing experimental measured data (Jin al. 2020).
We compare method with 3D molecular drug design models using both Target Set A and B since these models can be generalized to all targets. A comparison with 2D drug design models only conducted on Target Set due to their limited application scope.
'wheee A {0. 10, 20, 350]. Note that Makel is the neighhoring atom of tike in skeleton and Wing that of Ving in the added fragment shown in Figure top left

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Baselines We compare GEKO with the following baselines. liGAN (Masuda et al. 2020) is a drug design method that can generated 3D molecules conditioned on a target binding site. represents molecules atomic density grids, and uses conditional variational autoencoders in conjunction with a GAN loss to learn the distribution in existing ligand-target complexes. The atom types and 3D co- ordinates are determined by optimizing the fitness to the generated atomic density grids. JT-VAE (Jin et al., 2018a) represents 2D molecule as junction trees and uses variational autoencoders to generate new molecules. It employs Bayesian optimization to achieve property optimization. Ra- tionaleRL (Jin et al. (020) constructs new 2D molecules by extracting and combining rationales for desired properties. GA+D (Nigam et al. 2020) uses genetic algorithm (GA) to search for molecules with desired properties via explicit editing. It equips GA with deep neural network penalty to promote its exploratory behaviour and increase the diversity of the generated molecules. GraphAF (Shi et al. 2020) a flow-based autoregressive model for molecular graph generation. MolEvol (Chen) ct al. 202 uses an Expectation- Maximization (EM) like explainable evolutionary approach to op- timize molecular properties. Following the method of RationaleRL. it also extracts rationales and conducts a molecular graph completion. MolDON (Zhou et al. 2019) is a reinforcement learning- based molecular optimization method. MARS (Xie et al.) 2021 is sfate-of-the-an art method, which uses adaptive Markov chain Monte Carlo sampling to generate 2D molecules by explicit editing.
Evaluations We evaluate the models' performance from two aspects: the overall quality of the generated molecules and 2. the capability of generating highly active molecules.
For the overall quality, the generated molecules should cover a diverse chemical space and also not overlap with existing actives. In addition, the generated molecules should fulfill the basic require- ments for potential drug molecules. More specifically, we use the following metrics to measure the overall quality: Uniqueness (Unig) is the percentage of unique molecules among all generated ones. Novelty (Nov) measures the percentage of generated molecules with similarity less than 0.4 compared to its nearest neighbor in the existing actives for the target of interest (Olivecrona et al.) 2017). Similarity is calculated based on pairwise Tanimoto similarity using Morgan fingerprints (Rogers & Hahn 2010). Diversity (Div) measures the internal diversity of the generated molecules, which is formularized as vi(n 1ī Extwreg 1 sim(x, x²). Morgan fingerprint with radius of is used to calculate similarity. Success rate (SR) calculates the percentage of generated molecules that pass the predefined thresholds for desired properties. In this work, we define a successful molecule to have QED 2 0.25, SAscore 2 0.59, and Vinajock score < -8118 kcal/mol. QED and SAscore thresholds are defined as the 10th percentile of approved drugs in DrugCentral (Ursu et al., 2019) and the intuition is to cover majority of real drugs. Vina score is a binding energy evaluation us- ing the global energy optimization algorithm in Vina. Its threshold corresponds to a binding affinity less than 1 μ.M, which is widely used rule of thumb to guarantee moderate biological activity in medicinal chemistry. For 3D baselines, the generated 3D molecules are directly docked using Vina, while, for 2D baselines, the generated 2D molecules Kit and then docked using Vina. Product (Prod) is the product of the four aforementioned metrics, which serves as a comprehensive evaluation.
embedded into 3D conformers using RD-
To evaluate the models' capability of generating highly active molecules, we analyze the distribu- tion of biological activities using Vina global search reflected by Vinaaek score. Unlike QED and SAscore, Vinadock score is the higher the better. We use the Median Vinadock (Median) and the average of Top 10 Vinadock (Top 10) quantify the activity distribution.
Experimental Setup We generate 1000 molecules for each target and the target 3D structures are downloaded from CrossDocked2020 (Francoeur et al.) 2020). 3D rigid fragments are restricted to include no more than 10 heavy atoms and top 1000 most frequent fragments are selected as the fragment library. The generated molecules are restrict to contain no more than 40 heavy atoms. Starting from methane, 5000 Markov chains proceed simultaneously to ensure sufficient data for on- the-fly training. 1000 molecules are randomly selected at the last step for performance evaluation. HMPNN model contains 6 atomic layers and 3 fragment layers. The atomic node features includes atomic number, element type, charge and 3D coordinates, and the atomic edge features include bond type. The hidden layer node embedding has size of 64. For model training, we use Adam optimizer (Kingma & Ba 2013) to update the model's parameters with an initial learning rate of 3 x 10-1 The training dataset /) is set to have maximum size of 75000 samples.
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Table I: Comparison of different drug design methods
Uniq (%)
SR
Nov (%)
Div
Prod
Median (keal/mol)
Top 10
Targets
Method
(%)
(kcal/mol)
IGAN
99.8
1.0
100 1: 0.0
0.922 4: 0.001
0.01 0.01
-5.91
0.43
-8.79 0.71
Set
GEKO
100 0,0
$7.0 13.2
100 = 0.0
0.912 : 0.004
0.52 0.12
-9.58 + 1.01
-12.23 + 1.20
JT-VAE
0.2
11.3 0.8
100 = 0.0
0.910 + 0.001
0.10
-8.21 = 0.30
-11.38 +
RationaleRL
26.7 0.7
14.3
33.4 2.0
0.883 0.010
0.07 0.04
-7.76 0.21
-9.56
0.23
GA + D
3.7
23.7 + 20.9
86.5 13.5
0,849 + 0.012
0.05
-7.21 + 1.00
-9.44 1.04
Set B
GraphAF
0.4
0.3
100 0.0
0,946 +: 0.000
0.00
-4.51 = 0.18
-8.85
0.22
MolDQN
6.1
0.0
100.0
= 0.0
0.784 1 0.043
0.00
-5.54 2 0.19
6.95 0.13
MolEvol
97.8 1 0.4
4144 17.0
62.6
18.4
0.737 + 0.045
0.17
8.17 + 0.19
9.26 0.44
MARS
84.7 1.3
24.7
9.
0.266 0.002
0.19
-7.73
0.54
-
lIGAN
0.1
0.2 0.1
100 0.0
0.923 1 0.002
0.00
5.34 1 0.00
8.03 = 0.09
GEKO
100
57.1
100 + 0.0
0.910 + 0.001
0.52 0.02
0.14
-11.84 0.54
4.2 RESULTS AND ANALYSIS
We compare GEKO with baseline models on Target Set A and B and the average results over targets in each set are summarized in Table More detailed performance on each target can be found in the appendix. The performance of 2D molecular generation baselines, namely JAE, RationaleRL, GA + D. GraphAF, MolDQN, MolEvol and MARS, is only reported on Target Set B. From the results, it is obvious that GEKO outperforms baseline models all evaluation metrics except for diversity. In Target Set A, GEKO surpasses liGAN on the Prod score, which is an indicator for the chemical space exploration power. In Target Set B, it surpasses the best bascline MARS by 3-fold on the Prod score. In addition, GEKO achieves a median Vinaa score of -9.58 kcal/mol and -9.22 kcal/mol respectively in Target Set A and B. It is lower than the best-performing baseline JT--VEE by about kcal/mol, which corresponds to about 5-fold increase in the biological activity. In terms of diversity, GEKO obtains a comparable performance.
Among all these baseline methods, liGAN is the only 3D molecular generation method, but its performance is not satisfactory. liGAN uses 3D ligand-target complex structure database, Cross- Docked2020, as its training set, and employs VAE to learn the structural distribution of the 3D ligands. However, not all the molecules this dataset are drug-like or highly active. Therefore, is not guaranteed that the generated molecules can be better than an experimental measured active molecule especially without a designed optimization procedure in the 3D space.
2D drug design methods, due to their data driven nature, can only be applied to targets with rich ex- perimental data, which makes them unsuitable in many application cases. Also when using physico- chemical evaluation metrics, their performance can not be guaranteed. MARS achieves the best Prod score among all 2D drug design baselines. It demonstrates a moderate performance both chemical space exploration and capability of generating active molecules. MolEvol has the best success rate among all 2D drug design methods. However, as both MolEvol and RationaleRL extracts substruc- tures from existing actives rationales for property optimization, they suffer low novelty which hurts their overall performance. JT-VAE achieves good median and top 10 Vinaase scores, but it suffers greatly in the success rate. GA+D uses genetic algorithm search for molecules with de- sired properties. GA can easily get trapped in local minima, which leads to the low uniqueness. MolDQN suffers from low uniqueness and success rate. GraphAF has relatively weak performance on Prod score and Vinadock since many of its generated molecules are small and cannot meet the drug requirements.
We visualize the generated molecules and inspect their physical interactions with the corresponding targets. Figure 3 shows an example of GEKO generated molecule for MDM2 target (PDB ID: 40Q3). The binding site of MDM2 protein features three sub-pockets indicated as the colored region in Figure 3 Most MDM2 inhibitors has three arms to reach into the sub-pockets, and the binding mode of a strong MDM2 inhibitor is shown in Figure 3 left. Majority of the successful molecules generated by GEKO also have the three-arm structure and the binding mode of a candidate with highest Vinammn score is shown in Figure 3 middle and right. From the illustration, it is clear that the generated molecule has a good shape complimentarity with the target binding site, which is important for forming strong physical interactions.
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basting
Desigred Tup Vane
Designed Saie View
Figure 3: 3D Binding Pose Comparison between Existing Actives and Generated Molecules
4.3 ABLATION STUDY
In this section we study the contributions HMPNN model and weighted maximum likelihood esti- mation loss in the adaptive proposal using JNK3 target. The comparisons performed using 1000 sampling chains. Table summarizes the performance of different proposal strategies. Random represents proposal which randomly selects any bonds and fragments modify the molecular structure. HMPNN (Random Weight) represents HMPNN model with random weights. MPNN stands for using MPNN to propose editing operations, whereas, HMPNN indicates the use of hier- archical MPNN instead. Except for the Random proposal, all other proposals only allows editing on the fragment level as part of our architecture design. In addition, all proposals are equipped with simulated annealing for property optimization. The results indicates the adaptive proposal parame- terized using HMPNN is better than the random proposal reflected by the Prod score, median and top 10 Vinadock scores. HMPNN (Random Weight) shows better performance than Random proposal in the Prod score and obtains similar performances on Vinadock scores, which reflects editing on the fragment level is more effective in drug-like space exploration. Comparing to MPNN model, the addition of a fragment layer also leads to a performance increase Prod score and Top 10 Vinagock and achieves comparable result in Median Vinadock-
Table 2: Comparison of different proposal strategies
Uniq (%)
SR
Nov
Div
Prod
Median (kcal/mol)
Top 10 (kcal/mol)
Method
(%)
(%)
Random
100.0
31.4
100.0
0.909
0.29
-8.80
-12.09
HMPNN (Random Weight)
100
50.0
100
0.914
0.46
-8.91
-11.99
MPNN HMPNN
100
54.1
100
0.911
0.49
-9.26
-12.28
100
58.2
100
0.911
0.53
-9.24
-12.50
In addition, we compare the effects of different loss functions in training the adaptive proposal (Table 3. WMLE, on top of the standard ML.F, takes the score difference f(x') - f(x) into consideration. Using WMLE results in dramatic improvement on the model's performance. By explicitly formulating the objective score difference in the loss function, the model can more easily leamn edits leading to significant improvements than those with moderate boosts.
Table 3: Comparison of different loss functions
Uniq (%)
SR
Nov
Div
Prod
Median (kcal/mol)
Top 10 (kcal/mol)
Method
(%)
(%)
MLE
100
36.7
100
0.915
0.34
-8.59
-11.96
WMLE
100
58.2
100
0.911
0.53
-9.24
-12.50
CONCLUSION
In this paper, propose GEKO, which is generalizable model to design drug molecules condi- tioned on a target binding site. We formulate the drug design and optimization task as step-wise editing process and developed hierarchical MPNN model to learn the structure-activity relation and facilitate the design process. Our method shows great potential on 3D drug design tasks with 3-fold performance increase comparing the best baseline model.

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Under review as a conference paper at ICLR 2022
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Under review as a conference paper at ICLR 2022
A APPENDIX
Table 4: Comparison of methods on individual target in Target Set A
Uniq (%)
SR
Nov
Div
Prod
Median (kcal/mol)
Top 10
Method
Target
(%)
(%)
(kcal/mol)
3H7W
99.9
0.0
100.0
0.923
0.00
-5.33
-7.47
5E19
99.8
1.5
100.0
0.923
0.01
-6.11
-9.43
2RD6
100.0
1.0
100.0
0.921
0.01
-5.78
-9.11
3VRJ
99.7
3.1
100.0
0.922
0.03
-6.67
99.25
liGAN
40Q3
100.0
0.0
100.0
0.923
0.00
-5.0
-7.57
4CG9
99.6
0.8
100.0
0.922
0.01
-6188
-9.29
ЗРОР
99.9
0.7
100.0
0.920
0.01
-6.77
99.24
4PS7
99.9
0.4
100.0
0.923
0.00
-577
-8.60
5MKU
99.6
1.9
100.0
0.923
0.02
-6.19
-9.52
IFKG
100.0
0.1
100.0
0.921
0.00
-5.66
-8.47
3H7W
100.0
53.4
100.0
0.914
0.49
-8.52
11.05
5E19
100.0
69.7
100.0
0.916
0.64
-9.60
-12611
2RD6
100.0
50.3
100.0
0.908
0.46
-11.69
-1..3
3VRJ
100.0
67.9
100.0
0.919
0.62
9954
-1.21
GEKO
40Q3
100.0
40.6
100.0
0.909
0.37
-8.48
-10.78
4CG9
100.0
51.3
100.0
0.907
0.47
-9.89
-12.54
ЗРОР
100.0
67.7
100.0
0.916
0.62
-00.28
13.41
4PS7
100.0
61.6
100.0
0.907
0.56
-10.37
-12922
5MKU
100.0
75.6
100.0
0.916
0.69
-9.36
-12.28
IFKG
100.0
31.8
100.0
0.911
0.29
-8.10
-10.12
Table 5: Comparison of methods on individual target in Target Set B
Uniq
SR
Nov
Div
Prod
Median (kcal/mol)
Top
Method
Target
(%)
(%)
(%)
(kcal/mol)
JNK3
96.9
10.4
100.0
0.911
0.09
-7.91
-11.50
JT-VAE
GSK33
96.6
12.1
100.0
0.908
0.11
-8.50
-1.25
JNK3
98.1
41.0
31.4
0.873
0.11
-7.97
-9.78
RationaleRL
GSK33
99.4
12.4
35.3
0.893
0.04
-7.55
-9.33
JNK3
36.5
44.6
73.0
0.837
0.10
-8.21
-10.98
GA+D
GSK38
43.9
2.7
100.0
0.860
0.01
-6.21
-8.90
JNK3
90.6
0.1
100.0
0.946
0.00
-4.99
-8.63
GraphAF
GSK38
91.4
0.8
100.0
0.946
0.01
-4.33
-9.07
JNK3
50.2
0.0
100.0
0.827
0.00
-5.35
-6,82
MolDQN
GSK33
62.3
0.0
100.0
0.741
0.00
-5.74
-7.08
JNK3
98.2
58.4
44.2
0.782
0.20
-8.36
-9.70
MolEvol
GSK338
97.4
24.4
81.1
0.692
0.13
-7.98
-8.82
JNK3
83.4
54.9
91.0
0.825
0.34
-8,27
-10.12
MARS
GSK38
85.9
5.6
89.3
0.828
0.04
-7.19
-8.97
JNK3
99.8
0.1
100.0
0.921
0.00
-5.34
-7.94
liGAN
GSK38
99.9
0.2
100.0
0.924
0.00
-5.33
-8.12
JNK3
100.0
55.0
100.0
0.911
0.50
-9.36
-12.38
GEKO
GSK33
100.0
59.2
100.0
0.909
0.54
-9.07
-11.30
13

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Under review as a conference paper at ICLR 2022
We visualize the evolution of adaptive geometric editing proposal under knowledge guidance using IMPAAMccnnnes et al. 2018) Figure The illustration in Figure A shows chemical space coverage changes with steps. It indicates directional shift the Markov chains proceeding. Figure 4 B shows the objective score changes over steps, and a clear improvement of objective score is observed.
step 10
step 100
step 1000
2109
Figure 4: Visualization of Properties Changes Over Steps. A. Chemical Space Coverage. Visu- alization of chemical space coverage at step 10 (black), step 100 (yellow) and step 1000 (red). B.Objective Score.
14


Reviews:
[{"review_id": "tHZmLREqsRx", "reviewer": "Reviewer_gg4D", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "\nThe high level contours of the technical work sound reasonable;\nhowever, by reading the paper I was not convinced that the eventual\ndeep learning model adds much value to this process. If I take the\nauthors ablation study by heart, then a random way of picking editable\nbonds would still result in a fantastic score, way beyond any of the\nscores of previously published chemistry models, even those in the 2D\ncase. So is the result simply a tuned simulated annealing of fragments\nor is there any important learned aspect on picking from the library?\n\nUnfortunately, this paper is not written in a clear fashion and leaves\nopen questions relating to reproducibility. There is a lack of a clear\nexposition of the details with regards to the datasets, benchmarks,\nand tons of additional little details peppered throughout the paper:\ndoes the fingerprint used for similarity include chirality and what is\nits radius? How large was the resulting rigid fragment library? (At\nsome point the authors mention including only fragments with less than\n10 atoms, but even the single aromatic ring in Figure 1 has 12 atoms\ntotal---did the authors mean heavy atoms when they say atoms?) What\nwere the training/starting molecules in each case and do those matter\n(if not, do the authors start from a methane every time?) Since the\nauthors seem to have used docking together with the 2D models, why\ndidn't they also use the 2D generator models on the 3D benchmarks of\ntable 1? (Incidentally, in Table 1, the largest score in column Div\nseems to be the one for GraphAF, not liGAN.) The explanation of the\nauthors that GraphAF generated small molecules that don't meet the\ndrug requirements seems interesting; I thought that this model can be\ntrained to generate arbitrary large molecules---is that not the case?\nWhat targets did the authors evaluate in table 2, 3; and why are the\nbaseline results different from those in table 1? I could not find out\nwhat the scatterplot in Figure 4 represents---is that some abstract\nview of chemical space, and if so what exactly are the x- and y- axes?\nWhat is the deepSAR model in tables 4, 5? (I checked that the authors\ndon't refer to the paper with the model called deepSAR, but I suspect\nthat they might have meant GEKO in those tables; additionally, in\ntable 5, I suppose the authors meant to refer to Target Set B, not A).\n", "summary_of_the_paper": "The paper proposes a method that designs molecules by combining\nfragments along \"editable\" (single) bonds. The method uses two graph\nneural networks (on atoms and on fragments) to learn to pick editable\nbonds. The method also uses simulated annealing to guide the\ncombination of fragments (and possibly their dihedral angles) to\noptimize a weighted score that the authors have found helps their\ntraining. The model is able to propose reasonable molecules in 3D\nconformations within a ligand pocket using a docking score for\nguidance. The authors perform an ablation study that shows that even\nwithout the deep neural network for selecting the editable bonds,\ntheir sampling methodology performs rather well.\n", "summary_of_the_review": "\nI have concerns about the reproducibility of this work and I find a\nlot of details lacking. The English needs some work; some figures and\ntables have unexplained or mislabeled data. It is possible that with a\nlot of additional work the same idea could become clearer and well\ndocumented, but I am not confident it could make it to ICLR22.\n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Wp02sbxL6Vm", "reviewer": "Reviewer_ZrRD", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "The approach described appears plausible. The methods are mostly well described, and the paper is clearly written for the most part.\n\nAlthough the results are good, most of the proposals in the description of GEKO seem to be based on existing literature, and indeed described in the prior work section of this paper (e.g., MARS). So the paper seems less about a novel approach and more about SOTA performance. But the dataset used to test the model is a bit small if this is where the novelty of the paper lies. Only 12 targets is a very small dataset size when comparing model performance, and unlikely to be representative of the variation seen in a practical setting.\n\nHave molecular docking software to estimate binding energy - but how accurate are these? In general I found the description of docking to be limited.\n\nIn the ablation study, ‘Random’ is chosen as a kind of baseline, but the score is better than expected if just randomly picking editable bonds without any deep learning. Why is this? In general more analysis of the ablation study findings would be helpful.\n\nThe authors claim GEKO outperforms other methods significantly. But the claim of statistical significance is not backed up with p values or confidence intervals for the comparison. The authors should provide evidence of a statistically significant improvement over other models, or adjust the wording. \n\nThe authors claim 1D/2D approaches limit models to targets with rich activity data. This is not entirely true, as really we want models to be able to generalize to new, previously unseen targets. With a large enough dataset, this is not impossible. \n\n“Finding a drug molecule that can cure specific diseases is one of the most significant yet challenging task in human development” - it’s a very minor point, but the field of human development is unrelated to the field of drug discovery. The authors may want to say it is a major and significant challenge in the field of biology/medicine instead.", "summary_of_the_paper": "The authors propose an approach for drug design that generates molecules by simulating adding or deleting parts of the molecules, and using graphnets to capture atom and fragment level information and construct new molecules. They use simulated annealing to ‘edit’ the 3D structures, and docking simulations, drug-likeness and synthesizability to provide information back into training. In this way, the authors claim the model can be trained on self generated data. The authors compare with multiple baselines on a test set of 12 targets, including the current SOTA model, and report improved performance.", "summary_of_the_review": "While the results on the test set are good, I’m not convinced that the results alone here are novel enough for acceptance. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "LEwytm_vTLd", "reviewer": "Reviewer_ExEq", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "Strength:\n1. The proposed model outperforms other baselines in the multi-objective molecules optimization benchmark. \n2. This model doesn't rely on a data-driven biological activity predictor.\n\n\nWeakness:\n1. The model seems to be incremental. MARS already used rigid fragment library, editing operations, and MPNN. GEKO extends them to geometric editing, HMPNN, and docking software. At the first glance, I thought those rigid fragment library and editing operations are brand new concepts, but I found it's not after reading the MARS paper. It'd be nice if the authors contrasted GEKO and MARS so that readers would not be misled. \n2. There are always pros and cons for each direction. The good thing about using a data-driven biological activity predictor is that binding site information is not required. Therefore, it doesn't require the 3D structure of the target, which makes it to be applicable to many proteins. However, when using docking software, data-driven predictors of biological activity are not required, but the 3D structure and binding site information are essential.\n3. When running docking software, different poses of a molecule should be generated and tested. However, that information seems to be missing in the method section. In general, the pose generation is a big bottleneck, so if this information is included in the Methods section, it will help to better evaluate this paper.", "summary_of_the_paper": "This paper proposes a method to optimize the multiple properties of a molecule. They first define rigid fragment library as a building block, which is used to generate a new molecule through geometric editing. They represent a molecule using HMPNN and the whole model is trained using the weighted maximum likelihood estimation. The proposed model is compared with some baselines on several multi-objective optimization tasks and shows good performance.\n", "summary_of_the_review": "I have some questions/suggestions;\n1. Optimizing speed comparison would be a good addition because docking simulation is known to be very slow.  \n2. In equation (15), the angle is sampled from a softmax funciton? Is the angle categorical?\n3. In the baselines, it's known that VJTNN[1] is later model than JT-VAE. In addition, MolDQN[2] and CMG[3] are other recent models. Better to look at them.\n\n[1] Jin, Wengong, et al. \"Learning multimodal graph-to-graph translation for molecular optimization.\" arXiv preprint arXiv:1812.01070 (2018).\n\n[2] Zhou, Zhenpeng, et al. \"Optimization of molecules via deep reinforcement learning.\" Scientific Reports 9.1 (2019): 1-10.\n\n[3] Shin, Bonggun, et al. \"Controlled molecule generator for optimizing multiple chemical properties.\" Proceedings of the Conference on Health, Inference, and Learning. 2021.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "7wucx3T-oi", "reviewer": "Reviewer_X9Jm", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "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": "The main idea of the paper is summarized in the above sentences. The advantages and the disadvantages of this paper from my side are as follows. \n\nStrengths:\n1. This paper mainly focuses on knowledge-guided geometric editing. As for the knowledge, as far as I read, including (point out if I am not correct): autodock vina (for target-ligand energy), QED (drug-likeness), SAscore (for synthetic accessibility). The knowledge is important and necessary for molecule design. \n2. The geometric editing stands on the bonds in the molecule (between fragments), which causes stability and the specific property. The editing method is in a reasonable design, which selects the bond and the fragment, then generates a new molecule. \n3. The results on the selected target perform well in the 3D generation w.r.t the evaluation metrics. \n\nWeaknesses:\nThough this paper gives knowledge-based editing, there are many concerns I have, \n1. In the big picture, GEKO is a self-learning method based on the knowledge information provided by some softwares. Actually, the formulation of Equation (1) and Equation (16) is exactly the reinforcement learning-based method, or specifically, the policy gradient method. Equation (1) is indeed the reward function provided by the ground-truth (though provided by the software), and Equation (16) is the reward weighted objective. In this way, the claim of this paper that it is an unsupervised drug design is somehow overclaimed. Besides, the authors even do not talk about RL at all.\n2. Detailed modeling questions. I think $x_{frag}$ is different from the $x_{skel}$, which should be the new fragments in the fragment library. However, the definition of the softmax equation (10) can not reflect at all, and where is $x_{frag}$ in equation (10)? What is the detailed softmax? Similarly, in equation (13), where is r in the right side?\n3. I am confused about the edge r and the bond a, what is the difference? why there are two samples of r and a?\n4. As for the four atoms selected u, w (skel and frag), how are they selected? Since each bond only has two atoms, and what are the two other atoms selected? Footnote 4 is missing. \n5. In the overall formulation, the dimension of the formulation are not introduced, which makes feel confused about the transformation, for example, the $p_{attach}$. \n6. The training cost is also important since this is a self-learning method and the training data is generated by the model itself. Therefore, it is important to report the cost and the comparison with other baseline methods. \n7. More importantly, the following content of dihedral angle $\\alpha$ is missing. What is then after sampling an angle $\\alpha$?\n8. Minor points, some typos are existed, for example, 'seleted' in Figure 1. \n", "summary_of_the_paper": "This paper works on the deep learning models for automatic drug design. The authors propose a method named GEKO, the geometric editing under knowledge guidance, which incorporates the physicochemical knowledge into the designed 3D model. The authors claimed this is an unsupervised drug design method that the training is conducted in a purely training data free fashion. The training algorithm is based on geometric (3D) editing with the knowledge (Vina, QED, SA core) guidance by self-training and simulated annealing. The representation of the molecule is modeled at atom-level and fragment-level. With the geometric editing process continuing, the model continues updating and learning towards a better result. The method is evaluated on the drug design task on two sets of target datasets, by generating the molecules (drugs), the results are evaluated by multiple metrics. The performances are improved compared with other baseline methods (including both 2D and 3D methods). \n", "summary_of_the_review": "The method contains several signs of progress to generate new drugs in 3D space. Overall it is ok, but more questions should be answered. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The authors describes a drug design method that generates molecules by simulating adding or deleting parts of the molecules, and using graphnets to capture atom and fragment level information and construct new molecules.  Simulated annealing is used to ‘edit’ the 3D structures, and docking simulations, drug-likeness and synthesizability are used to provide information back into training.  The authors compare with multiple baselines on a test set of 12 targets, including the current SOTA model, and report improved performance.

Strengths:

- The proposed model outperforms other baselines in the multi-objective molecules optimization benchmark.
- The model doesn't rely on a data-driven biological activity predictor.

Weaknesses:

- The reviewers point out that the model seems to be incremental with respect to previous work.
- The reviewers have concernts about the reproducibility of the work and find a lot of details lacking.

This is a borderline paper with a majority of reviewers voting for rejection. I recommend the authors to addrses the weaknesses above and resubmit to another venue.
Decision: Reject