Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning (2004.12485v2)

Abstract: Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models. However, current generative approaches exhibit a significant challenge as they do not ensure that the proposed molecular structures can be feasibly synthesized nor do they provide the synthesis routes of the proposed small molecules, thereby seriously limiting their practical applicability. In this work, we propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design, Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo drug design system. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space by subjecting commercially available small molecule building blocks to valid chemical reactions at every time step of the iterative virtual multi-step synthesis process. The proposed environment for drug discovery provides a highly challenging test-bed for RL algorithms owing to the large state space and high-dimensional continuous action space with hierarchical actions. PGFS achieves state-of-the-art performance in generating structures with high QED and penalized clogP. Moreover, we validate PGFS in an in-silico proof-of-concept associated with three HIV targets. Finally, we describe how the end-to-end training conceptualized in this study represents an important paradigm in radically expanding the synthesizable chemical space and automating the drug discovery process.

Overview of Reinforcement Learning for Forward Synthesis in Drug Design

The paper "Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning" proposes a novel reinforcement learning (RL) framework for de novo drug design. This paper addresses the fundamental limitation of existing generative models in ensuring that proposed molecular structures can be feasibly synthesized. The authors introduce a forward synthesis approach, termed Policy Gradient for Forward Synthesis (PGFS), embedding the concept of synthetic accessibility directly within the molecular design process.

Problem Statement and Solution

Current generative models for molecular design, including string-based and graph-based frameworks, struggle with the issue of synthetic feasibility. This shortcoming limits their practical application, as many generated molecules cannot be produced using available chemical reactions. PGFS overcomes this by employing RL to navigate the vast, synthetically accessible chemical space. The RL agent uses commercially available small molecule building blocks and selects valid chemical reactions to iteratively synthesize drug-like molecules.

In this framework, the problem is defined as a Markov decision process where each state represents a product molecule and actions correspond to selecting appropriate reactants and reaction templates. The agent's goal is to maximize a reward function related to the drug-likeness and synthetic accessibility of the proposed molecule.

Methodology

PGFS utilizes a policy gradient approach where the RL agent learns from its environment by interacting with a virtual multi-step synthesis process. The framework involves hierarchical action spaces, where first a reaction template is selected, followed by the selection of a compatible reactant molecule. This hierarchical setup helps manage the complexity of the large state and action spaces inherent in chemical synthesis.

To efficiently explore the continuous action space formed by the large set of possible reactants, PGFS employs techniques such as k-nearest neighbors for mapping continuous embeddings to discrete molecular structures. The adoption of techniques from continuous action space RL methods, such as Twin Delayed Deep Deterministic policy gradient (TD3), further enhances learning efficiency.

Numerical Results and Validation

The PGFS demonstrates state-of-the-art performance on standard benchmarks, such as quantitative estimate of drug-likeness (QED) and penalized logarithmic partition coefficient (clogP), surpassing existing models like Genetic Algorithms, Deep Generative Models, and RL-based graph modification approaches. The paper also validates this approach on HIV-related biological targets, showing superior predictive QSAR modeling performance against baseline methodologies.

The results indicate that PGFS not only excels in maximizing the reward metrics but also ensures the generated molecules are synthesizable. The reported numerical benefits suggest that embedding synthetic knowledge directly into molecule generation represents a promising strategy for expanding the synthesizable chemical space.

Implications and Future Work

The implications of this research are profound, particularly in automating and accelerating the drug discovery process. By ensuring synthetic feasibility, PGFS could significantly reduce the gap between computational predictions and experimental validations, thus narrowing the drug development pipeline. Theoretically, this approach opens new avenues for integrating RL with cheminformatics, offering a scalable solution for exploring chemical spaces.

Future developments could explore enhancing the policy and value networks, integrating more sophisticated synthetic knowledge systems, and applying this method to multi-objective tasks in drug discovery. Additionally, addressing the stereoselectivity in reaction predictions and further reducing computational demands during training remain promising directions.

In conclusion, PGFS represents a pivotal step towards addressing the complexities of de novo drug design. By leveraging RL to navigate synthetically accessible spaces, it paves the way for more practical, efficient, and reliable generative models in pharmaceutical chemistry.