GraphXForm: Graph transformer for computer-aided molecular design

• URL: http://arxiv.org/abs/2411.01667v2
• Date: Thu, 20 Mar 2025 12:01:38 GMT
• Title: GraphXForm: Graph transformer for computer-aided molecular design
• Authors: Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf, Martin Grohe, Jakob Burger, Alexander Mitsos, Dominik G. Grimm,
• Abstract summary: We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds.<n>We evaluate it on various drug design tasks, demonstrating superior objective scores compared to state-of-the-art molecular design approaches.
• Score: 73.1842164721868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative deep learning has become pivotal in molecular design for drug discovery, materials science, and chemical engineering. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on specific objectives. However, string-based models face challenges in ensuring chemical validity and enforcing structural constraints like the presence of specific substructures. We propose to instead combine graph-based molecular representations, which can naturally ensure chemical validity, with transformer architectures, which are highly expressive and capable of modeling long-range dependencies between atoms. Our approach iteratively modifies a molecular graph by adding atoms and bonds, which ensures chemical validity and facilitates the incorporation of structural constraints. We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned using a new training algorithm that combines elements of the deep cross-entropy method and self-improvement learning. We evaluate GraphXForm on various drug design tasks, demonstrating superior objective scores compared to state-of-the-art molecular design approaches. Furthermore, we apply GraphXForm to two solvent design tasks for liquid-liquid extraction, again outperforming alternative methods while flexibly enforcing structural constraints or initiating design from existing molecular structures.

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