Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation

• URL: http://arxiv.org/abs/2506.15309v1
• Date: Wed, 18 Jun 2025 09:39:51 GMT
• Title: Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation
• Abstract summary: We propose a structured active learning paradigm to balance chemical diversity, molecular quality, and multi-target affinity.<n>Our method alternates between expanding chemically feasible regions of latent space and progressively constraining molecules.<n>We demonstrate that careful timing and strategic placement of chemical filters within this active learning pipeline markedly enhance exploration of beneficial chemical space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Simultaneously optimizing molecules against multiple therapeutic targets remains a profound challenge in drug discovery, particularly due to sparse rewards and conflicting design constraints. We propose a structured active learning (AL) paradigm integrating a sequence-to-sequence (Seq2Seq) variational autoencoder (VAE) into iterative loops designed to balance chemical diversity, molecular quality, and multi-target affinity. Our method alternates between expanding chemically feasible regions of latent space and progressively constraining molecules based on increasingly stringent multi-target docking thresholds. In a proof-of-concept study targeting three related coronavirus main proteases (SARS-CoV-2, SARS-CoV, MERS-CoV), our approach efficiently generated a structurally diverse set of pan-inhibitor candidates. We demonstrate that careful timing and strategic placement of chemical filters within this active learning pipeline markedly enhance exploration of beneficial chemical space, transforming the sparse-reward, multi-objective drug design problem into an accessible computational task. Our framework thus provides a generalizable roadmap for efficiently navigating complex polypharmacological landscapes.

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