Volume 5 Number 1 2023
Generative Flow Networks in Molecular Generation: Stochastic Sampling, Multi-objective Optimization, and Drug Discovery Applications
Mohd Hasan Mujahid 1, Salaman Ahamad 1, Shaista Fatima 1*
Bioinfo Chem 5 (1) 1-8 https://doi.org/10.25163/bioinformatics.5110725
Submitted: 19 June 2023 Revised: 10 August 2023 Published: 16 August 2023
Generative Flow Networks (GFlowNets) are emerging as a promising framework for molecular generation and drug discovery, addressing key challenges in exploring vast chemical space. Despite significant advances in computational drug design, traditional approaches—including variational autoencoders, reinforcement learning, and Markov Chain Monte Carlo methods—often struggle to balance diversity and optimization, leading to limited exploration of high-quality molecular candidates. This review examines how Generative Flow Networks introduce a stochastic sampling paradigm in which molecular structures are generated proportionally to a defined reward function. Unlike conventional optimization methods that converge toward a single solution, GFlowNets maintain a diverse distribution of candidate molecules, enabling more effective exploration of chemical space. This property is particularly advantageous in multi-objective optimization settings, where factors such as binding affinity, drug-likeness, and synthetic feasibility must be simultaneously considered. Across recent studies, GFlowNets demonstrate improved performance in molecular generation tasks, offering a balance between exploration and exploitation that is difficult to achieve with existing machine learning approaches. However, challenges remain, particularly in reward function design, proxy model accuracy, and computational scalability. Overall, Generative Flow Networks represent a shift toward stochastic, distribution-based molecular discovery, providing a flexible and scalable framework for navigating chemical space in drug discovery and AI-driven molecular design.
Keywords: Generative Flow Networks; Molecular Discovery; Stochastic Sampling; Drug Design; Multi-objective Optimization
M.H.M. conceptualized the study, designed the review framework, and drafted the original manuscript. S.A. conducted literature analysis, contributed to data synthesis, and assisted in manuscript preparation. S.F. supervised the study, contributed to interpretation of findings, and critically reviewed and edited the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.
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