Molecular Inverse-Design Platform for Material Industries
원문 초록
The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance. However, the extra-vast combinatorial design space of material structures exceeds human experts' capability to explore all, thereby hampering material development. In this paper, we present a material industry-oriented web platform of an AI-driven molecular inverse-design system, which automatically designs brand new molecular structures rapidly and diversely. Different from existing inverse-design solutions, in this system, the combination of substructure-based feature encoding and molecular graph generation algorithms allows a user to gain high-speed, interpretable, and customizable design process. Also, a hierarchical data structure and user-oriented UI provide a flexible and intuitive workflow. The system is deployed on IBM's and our client's cloud servers and has been used by 5 partner companies. To illustrate actual industrial use cases, we exhibit inverse-design of sugar and dye molecules, that were carried out by experimental chemists in those client companies. Compared to general human chemist's standard performance, the molecular design speed was accelerated more than 10 times, and greatly increased variety was observed in the inverse-designed molecules without loss of chemical realism.
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Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
A Simple and Scalable Representation for Graph Generation
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
Learning-Order Autoregressive Models with Application to Molecular Graph Generation
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
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