A Transformer-based Generative Model for De Novo Molecular Design

• URL: http://arxiv.org/abs/2210.08749v1
• Date: Mon, 17 Oct 2022 05:03:35 GMT
• Title: A Transformer-based Generative Model for De Novo Molecular Design
• Authors: Wenlu Wang, Ye Wang, Honggang Zhao and Simone Sciabola
• Abstract summary: We propose a Transformer-based deep model for de novo target-specific molecular design. The proposed method is capable of generating both drug-like compounds and target-specific compounds.
• Score: 4.6782243206450325
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning draws a lot of attention as a new way of generating unseen structures for drug discovery. We propose a Transformer-based deep model for de novo target-specific molecular design. The proposed method is capable of generating both drug-like compounds and target-specific compounds. The latter are generated by enforcing different keys and values of the multi-head attention for each target. We allow the generation of SMILES strings to be conditional on the specified target. The sampled compounds largely occupy the real target-specific data's chemical space and also cover a significant fraction of novel compounds.

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