Interpreting Molecule Generative Models for Interactive Molecule Discovery

1 INTRODUCTION . Designing molecules with desired properties is a fundamental problem in chemistry , which has a variety of applications in drug discovery and material science ( Chen et al. , 2018 ) . Traditional pipelines require exhaustive human efforts and domain knowledge , which are difficult to scale up . Recent studies exploit deep generative models to solve this problem by encoding molecules into a latent space , from which random samples are drawn and decoded to novel molecules ( Walters & Barzilay , 2020 ) . It has been widely observed that such deep molecule generative models are able to facilitate the design and development of drugs and materials from many perspectives ( Lopez et al. , 2020 ; Sanchez-Lengeling & Aspuru-Guzik , 2018 ) . Despite the promising results of deep generative models for molecule generation , much less effort has been made to interpret the learned representations . Most of the existing models are based on deep neural networks , which are known to be short on interpretability ( Samek et al. , 2019 ) . Outside of molecule generation domain , many attempts have been made to improve the interpretability of deep learning models from various aspects , e.g. , representation space ( Zhou et al. , 2016 ) , model space ( Guo et al. , 2021 ) , and latent space ( Shen et al. , 2020 ; Shen & Zhou , 2021 ) . In molecule generation domain , interpretability can be studied in two ways : ( 1 ) the interpretation of learned latent space where steering the value of latent vectors could lead to smooth and continuous molecular property change and ( 2 ) the interpretation of molecular space that adjusting the molecular property could observe smooth structure change of molecules . In addition , it remains challenging to generate molecules with desired properties . Previous works mostly rely on optimization-based , reinforcement learning-based , and searching-based methods to achieve property control of the generated molecules ( Shi et al. , 2020 ; Jin et al. , 2018a ) . Specifically , reinforcement learning-based algorithm ( You et al. , 2018a ) equips the model with rewards designed to encourage the molecule generative models to generate molecules with specific molecular properties . Optimization-based algorithm takes advantage of the learnt latent space by molecule generative models and optimize the molecular properties via Bayesian Optimization ( Liu et al. , 2018 ) . Searching-based algorithm instead searches directly from the chemical space for molecules with optimal properties ( Kwon et al. , 2021 ) . However , these lines of work are designed for molecule generation with optimized property and thus unable to change the property monotonically and smoothly . Besides , current methods are confined to a limited number of molecular properties , which hinders real-world applications in drug discovery and material science . For example , existing work only discover a limited set of molecular properties , such as penalized logP ( octanol-water partition coefficient ) , QED ( Drug-likeness ) , DRD2 activity , etc ( Jin et al. , 2018a ; Shi et al. , 2020 ; Liu et al. , 2018 ; Fu et al. , 2020 ) . Consequently , when molecules with new properties are needed , the models must be re-trained with a different optimization goal , which is significantly time-consuming . To tackle the above challenges , we formulate a new task , molecule manipulation , which aims to improve the interpretability and steerability of a given molecule generative model via continuously manipulating molecular properties . Based on the observation that molecules sharing similar structures/properties tend to cluster in the latent space , we develop MolSpace Explorer , a model-agnostic method to manipulate molecules with continuous changes of molecular properties . Specifically , MolSpace Explorer first identifies the property separation hyperplane which defines the boundary for molecular properties ( e.g. , drug-like or drug-unlike ) in the latent molecular space learned by a given generative model . Based on the property separation hyperplane , we estimate the latent directions that govern molecular properties , which are in turn used to enable continuous change of the molecular structures and properties without re-training the given molecular generative model . To the best our knowledge , this work is one of the earliest attempts to achieve interactive molecule discovery through the steering of pretrained generative models . The experiments demonstrate that our method can effectively quantify the interpretability and steerability of state-of-the-art molecule generative models . To measure the ability of generative models in interpreting molecular properties and generating molecules with continuous property control , we design a new evaluation metric named success rate , which evaluates the percentage of successful manipulations with continuous property-changing molecules over manipulations of a group of molecules . To visualize our method and facilitate interactive molecule discovery for scientists , we develop an interactive system with visualization of real-time molecule manipulations and smooth molecular structure/property changes . Our main contributions are summarized as follows : • We formulate molecule manipulation , a new task which measures the interpretability and steerability of molecule generative models via the ability to manipulate the molecular properties of molecules in the latent space . • We develop a simple yet effective model-agnostic method named MolSpace Explorer for molecule manipulation , which further analyzes current molecule generative models in terms of their interpretability and steerability.terpretation . • Comprehensive experiments demonstrate the effectiveness of our method in quantifying the interpretability and steerability of various molecule generative models . An interactive system is developed for real-time molecule manipulation . 2 RELATED WORK . Molecule Generation . Recent studies have explored a variety of deep generative models for molecule generation . Specifically , GrammarVAE ( Kusner et al. , 2017 ) designs a variational autoencoder-based model that represents molecules as SMILE strings . With the advancement of graph neural networks ( GNN ) , a surge of GNN-based generative models have been proposed to tackle the problem , by combining GNN with variational autoencoders ( VAEs ) , generative adversarial networks ( GANs ) , normalizing flows , energy-based models ( EBMs ) , and reinforcement learning ( Olivecrona et al. , 2017 ; De Cao & Kipf , 2018 ; Jin et al. , 2018a ; Zhou et al. , 2019 ; Madhawa et al. , 2019 ; Shi et al. , 2020 ; Luo et al. , 2021 ; Liu et al. , 2021 ; Yang et al. , 2021 ) . To be specific , JT-VAE ( Jin et al. , 2018a ) proposes a VAE-based architecture to encode both atomic graphs and structural graphs for efficient molecule generation . MolGAN ( De Cao & Kipf , 2018 ) exploits GANs for molecule generation , where discriminators are used to encourage the model to generate realistic and chemically-valid molecules . MRNN ( Popova et al. , 2019 ) extends the idea of GraphRNN ( You et al. , 2018b ) to formulate molecule generation as an auto-regressive process . GCPN ( You et al. , 2018a ) formulates the molecule generation process as a reinforcement learning problem where it obtains a molecule step by step by connecting atoms and reward is used for controllable generation . GraphNVP ( Madhawa et al. , 2019 ) first introduces normalizing flows for molecule generation , where the generation process is invertible . Later work improve the flow-based models via autoregressive generation ( Shi et al. , 2020 ) , valency correction ( Zang & Wang , 2020 ) , and discrete latent representation ( Luo et al. , 2021 ) . GraphEBM ( Liu et al. , 2021 ) introduces energy-based models which models the density of molecule data . Controllable Molecule Generation . Another key point for molecule generation is controllable generation where the generated molecules are expected to possess certain properties . Early work ( Segler et al. , 2018 ) bias on the distribution of the data and fine-tune the generative models with known desired properties to generate molecules with desired properties . The recent work mainly leverage optimization-based ( Shi et al. , 2020 ; You et al. , 2018a ; Hoffman et al. , 2020 ; Winter et al. , 2019 ; ? ) , reinforcement learning-based ( Zang & Wang , 2020 ; Jin et al. , 2018a ; Blaschke et al. , 2020 ) , and searching-based ( Brown et al. , 2019 ; Yang et al. , 2020 ; Kwon et al. , 2021 ) approaches to generate molecules with desired properties . Optimization-based methods are quite flexible and can both directly work on the molecules ( Renz et al. , 2019 ; Fu et al. , 2020 ; Xie et al. , 2021 ; Maziarz et al. , 2021 ) and work on the learnt latent vectors of the molecules ( Gómez-Bombarelli et al. , 2018 ; Jin et al. , 2018b ; Winter et al. , 2019 ; Griffiths & Hernández-Lobato , 2020 ; Notin et al. , 2021 ) . Reinforcement learning-based methods usually formulates controllable generation as a sequential decision-making problem and requires a score-function to provide rewards to the agent . Searching-based approaches ( Brown et al. , 2019 ; Yang et al. , 2020 ; Kwon et al. , 2021 ) are also capable of searching molecules with optimized properties . Besides , few work ( Chenthamarakshan et al. , 2020 ; Das et al. , 2021 ) leverage the learnt latent space and achieve controllable generation by accepting/rejecting sampled molecules based on a molecular property predictor . Despite the ability to generate molecules with optimized properties , it is challenging for existing methods to interpret the generation process and can not generate molecules with monotonically and smoothly changing molecular properties . 3 PRELIMINARIES . Molecule Graph . Molecules can be presented as graphs X = ( V , E , E , F ) , where V denotes a set of N vertices ( i.e. , atoms ) , E ⊆ V × V denotes a set of edges ( i.e. , bonds ) , F ∈ { 0 , 1 } N×D denotes the node features ( i.e. , atom types ) and E ∈ { 0 , 1 } N×N×K denotes the edge features ( i.e. , bond types ) . The number of atom types and bond types are denoted by D and K , respectively . Deep Molecule Generative Models . In molecule generation , a generative model M encodes the molecular graph X as a latent vector Z ∈ Rl with l being the dimension of the latent space , and is capable of decoding any latent vector back to the molecular space . Specifically , variational autoencoder ( VAE ) ( Kingma & Welling , 2013 ) and flow-based model ( Flow ) ( Rezende & Mohamed , 2015 ) are the two most commonly used models for molecule generation tasks . Both of them encode the data from molecular space to latent space , which is usually modeled as a Gaussian distribution ; then they decode the latent code back to molecular space . They can be formulated as : z = f ( x ) , x′ = g ( z ) , ( 1 ) where x and x′ are the ground-truth and reconstructed/sampled data respectively , and z ∈ Z represents a latent vector in the latent space . 4 PROBLEM FORMULATION OF MOLECULE MANIPULATION . To improve the steerability and interpretability of molecule generative models , we propose a new research task , molecule manipulation , which interprets the generative model and steers the properties of the output molecules . To be specific , a deep generative model contains a generator g : Z → X , whereZ ∈ Rl stands for the l-dimensional latent space , which is commonly assumed to be Gaussian distribution ( Kingma & Welling , 2013 ; Rezende & Mohamed , 2015 ) . There exist property functions fP which define the property space P via P = fP ( X ) . drug-dislike mols Formulation . The input to molecule manipulation is a list of n molecules X = { x1 , x2 , · · · , xn } and a list of m molecular properties P = { p1 , p2 , · · · , pm } . We aim to manipulate one or more molecular properties p of a given molecule in a k consecutive steps and output the manipulated molecules with properties p′ = { p ( 1 ) , p ( 2 ) , · · · , p ( k ) } . By manipulating the given molecule , we can observe the alignment of Z → X → P , where the relationship between Z and X explains the latent space of molecule generative models . The relationship between X and P reveals the correlations between molecular structures and properties . By traversing latent space , we can generate molecules with continuous structure/property changes . Evaluation . For the molecule manipulation task , we design two new evaluation metrics named success rate ( SR ) and soft success rate ( SSR ) that measure the performance in discovering latent molecular property directions . To be specific , we consider a manipulation to be successful only if we generate molecules with monotonically-changing properties in a consecutive k steps of manipulation , as follows : φsuccess ( x , k ) = 1 [ ∀ i ∈ [ k ] , s.t. , fp ( x ( i ) ) − fp ( x ( i+1 ) ) ≤ 0 ] , ( 2 ) where fp is a property function which calculates certain molecular property and x ( i ) , x ( i+1 ) represents molecules generated in two adjacent steps . As monotonicity is rather strict , we propose a more flexible definition of success , namely soft success , as follows : φsoft success ( x , k ) = 1 [ ∀ i ∈ [ k ] , s.t. , fp ( x ( i ) ) − fp ( x ( i+1 ) ) ≤ ] , ( 3 ) where is a predefined tolerance threshold that weakens the monotonicity requirement . To extend the evaluation metric for |P | molecular properties and |X| candidate molecules to manipulate , we calculate the overall SR as : SR ( P , X , k ) = ∑ p∈P , x∈X 1 [ φsuccess ( xp , k ) ∧ φSD ( xp , k ) ∧ φDIV ( xp , k ) ] |P | × |X| , ( 4 ) where xp represents manipulating property p of molecule x which results in a manipulation path xp = { x ( i ) p |i ∈ [ k ] } . Since the molecular space is essentially discrete , we allow the model to generate duplicate molecules during manipulation , but the model has to generate at least one distinct molecule from the base molecule ( diversity or DIV ) and the structure difference ( SD ) enforces monotonically-decreasing structure similarity along the manipulation sequence , as follows : φDIV ( x , k ) = 1 [ ∃ i ∈ [ k ] , s.t. , x ( i ) 6= x ( 1 ) ] , ( 5 ) φSD ( x , k ) = 1 [ ∀ i ∈ [ k ] , s.t. , δ ( x ( i ) , x ( 1 ) ) − δ ( x ( i+1 ) , x ( 1 ) ) ≥ 0 ] , ( 6 ) where δ measures the structure similarity between the two input molecules . Similarly , we provide a soft structure difference ( SSD ) with a predefined tolerance threshold γ as follows : φSSD ( x , k ) = 1 [ ∀ i ∈ [ k ] , s.t. , δ ( x ( i ) , x ( 1 ) ) − δ ( x ( i+1 ) , x ( 1 ) ) ≥ γ ] , ( 7 ) Finally , we calculate the overall SSR as : SSR ( P , X , k ) = ∑ p∈P , x∈X 1 [ φsoft success ( xp , k ) ∧ φSSD ( xp , k ) ∧ φDIV ( xp , k ) ] |P | × |X| , ( 8 ) ‘

Summary: This paper presents a method named "MolSpaceExplorer" that explores the latent space of a molecule generative model to continuously optimize molecules toward despred properties.They identify latent directions by constructing a serapation boundary (hyperplane) on the latent space and use the directions to improve latent vectors, which are then fed into the generatove model to produce new molecules with desired properties. The authors also developed an interface for interactive molecular discovery. The authors mentioned that the main contributions of this study are 1) this method is model-agnostic thus applicable to any molecule generative model 2) it does not require any retraining of the molecule generative model.