2024/10/14 by Hampus Gummesson Svensson, Svensson, Hampus Gummesson, Christian Tyrchan +5 · 2 citations
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Biomolecules (q-bio.BM) #Computational Drug Discovery Methods #Evolutionary Algorithms and Applications #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2410.10431
openalex publication_date 2024/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
Fine-tuning a pre-trained generative model has demonstrated good performance in generating promising drug molecules. The fine-tuning task is often formulated as a reinforcement learning problem, where previous methods efficiently learn to optimize a reward function to generate potential drug molecules. Nevertheless, in the absence of an adaptive update mechanism for the reward function, the optimization process can become stuck in local optima. The efficacy of the optimal molecule in a local optimization may not translate to usefulness in the subsequent drug optimization process or as a potential standalone clinical candidate. Therefore, it is important to generate a diverse set of promising molecules. Prior work has modified the reward function by penalizing structurally similar molecules, primarily focusing on finding molecules with higher rewards. To date, no study has comprehensively examined how different adaptive update mechanisms for the reward function influence the diversity of generated molecules. In this work, we investigate a wide range of intrinsic motivation methods and strategies to penalize the extrinsic reward, and how they affect the diversity of the set of generated molecules. Our experiments reveal that combining structure- and prediction-based methods generally yields better results in terms of diversity.