  Structure-based drug design (SBDD) aims to discover drug candidates by
finding molecules (ligands) that bind tightly to a disease-related protein
(targets), which is the primary approach to computer-aided drug discovery.
Recently, applying deep generative models for three-dimensional (3D) molecular
design conditioned on protein pockets to solve SBDD has attracted much
attention, but their formulation as probabilistic modeling often leads to
unsatisfactory optimization performance. On the other hand, traditional
combinatorial optimization methods such as genetic algorithms (GA) have
demonstrated state-of-the-art performance in various molecular optimization
tasks. However, they do not utilize protein target structure to inform design
steps but rely on a random-walk-like exploration, which leads to unstable
performance and no knowledge transfer between different tasks despite the
similar binding physics. To achieve a more stable and efficient SBDD, we
propose Reinforced Genetic Algorithm (RGA) that uses neural models to
prioritize the profitable design steps and suppress random-walk behavior. The
neural models take the 3D structure of the targets and ligands as inputs and
are pre-trained using native complex structures to utilize the knowledge of the
shared binding physics from different targets and then fine-tuned during
optimization. We conduct thorough empirical studies on optimizing binding
affinity to various disease targets and show that RGA outperforms the baselines
in terms of docking scores and is more robust to random initializations. The
ablation study also indicates that the training on different targets helps
improve performance by leveraging the shared underlying physics of the binding
processes. The code is available at
https://github.com/futianfan/reinforced-genetic-algorithm.

Quantitative Methods;Machine Learning