EXPLORING CHEMICAL SPACE WITH SCORE-BASED OUT-OF-DISTRIBUTION GENERATION

Abstract

A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set. To generate truly novel molecules with completely different structures that may have even better properties than known molecules for de novo drug discovery, more powerful exploration in the chemical space is necessary. To this end, we propose Molecular Out-Of-distribution Diffusion (MOOD), a novel score-based diffusion scheme that incorporates out-ofdistribution (OOD) control in the generative stochastic differential equation (SDE) with simple control of a hyperparameter, thus requires no additional computational costs unlike existing methods (e.g., RL-based methods). However, some novel molecules may be chemically implausible, or may not meet the basic requirements of real-world drugs. Thus, MOOD performs conditional generation by utilizing the gradients from a property prediction network that guides the reverse-time diffusion process to high-scoring regions according to multiple target properties such as protein-ligand interactions, drug-likeness, and synthesizability. This allows MOOD to search for novel and meaningful molecules rather than generating unseen yet trivial ones. We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool.

1. INTRODUCTION

Finding novel molecules with desired chemical properties is the primary goal of drug discovery. However, the chemical space is vast, and it is infeasible to examine all possible molecules to find those satisfying a target molecule profile. Recently, deep molecule generation models that can automatically generate candidate molecules arose as promising substitutes (Gómez-Bombarelli et al., 2016; Lim et al., 2018; Schwalbe-Koda & Gómez-Bombarelli, 2019) for conventional experimental drug discovery approaches via trial-and-error processes with human efforts. However, most existing molecule generation models have the following two limitations, which limit their practical impact. First of all, the common pitfall of the models based on distributional learning is that the exploration is confined to the training distribution, and the generated molecules highly resemble those in the training set. For example, Walters & Murcko (2020) point out that the top-scoring molecule found by the model of Zhavoronkov et al. (2019) exhibits "striking similarity" to known active molecules included in the training set (see Figure 1 (Left; a1, a2)). This highly limits its applicability to de novo drug discovery which aims to find completely new molecules rather than slight variations of existing ones, emphasizing the need for a generation strategy that can generate out-of-distribution (OOD) molecules with desired properties. Secondly, there exists a discrepancy between the target chemical properties of the molecule generation models and those in real-world scenarios. The most common properties utilized by the molecule generation models are penalized logP and quantitative estimate of drug-likeness (QED) (Jin et al., 2018; You et al., 2018; Shi et al., 2019; Zang & Wang, 2020; Luo et al., 2021c; Liu et al., 2021) . However, as criticized by Coley (2020), Cieplinski et al. (2020), and Xie et al. (2020) , optimization of these scores may not lead to the discovery of useful drugs. For example, the top-scoring molecule found in terms of penalized logP in the state-of-the-art model is a trivial long chain of the maximum number of carbons (Luo et al., 2021c) , since penalized logP prefers large molecules.

