It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design

• URL: http://arxiv.org/abs/2410.11527v1
• Date: Tue, 15 Oct 2024 11:59:51 GMT
• Title: It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design
• Authors: Jeff Guo, Philippe Schwaller,
• Abstract summary: Constrained synthesizability is an unaddressed challenge in generative molecular design. We propose a novel reward function called TANimoto Group Overlap (TANGO) TANGO transforms a sparse reward function into a dense and learnable reward function -- crucial for reinforcement learning.
• Score: 0.4037357056611557
• License:
• Abstract: Constrained synthesizability is an unaddressed challenge in generative molecular design. In particular, designing molecules satisfying multi-parameter optimization objectives, while simultaneously being synthesizable and enforcing the presence of specific commercial building blocks in the synthesis. This is practically important for molecule re-purposing, sustainability, and efficiency. In this work, we propose a novel reward function called TANimoto Group Overlap (TANGO), which uses chemistry principles to transform a sparse reward function into a dense and learnable reward function -- crucial for reinforcement learning. TANGO can augment general-purpose molecular generative models to directly optimize for constrained synthesizability while simultaneously optimizing for other properties relevant to drug discovery using reinforcement learning. Our framework is general and addresses starting-material, intermediate, and divergent synthesis constraints. Contrary to most existing works in the field, we show that incentivizing a general-purpose (without any inductive biases) model is a productive approach to navigating challenging optimization scenarios. We demonstrate this by showing that the trained models explicitly learn a desirable distribution. Our framework is the first generative approach to tackle constrained synthesizability.

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