REACTCLASS: Cross-Modal Supervision for Subword-Guided Reactant Entity Classification

Wang, Xuan; Hu, Vivian; Jiang, Minhao; Xiao, Jinfeng; Loving, Danielle C.; Ji, Heng; Burke, Martin D.

doi:10.1109/bibm55620.2022.9995489

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…In Thrust 1, we are focusing on the development of AlphaSynthesis. We are advancing foundational AI research, as highlighted by multiple papers accepted and published by top tier journals such as Science and Nature Catalysis , and top AI conferences (Angello et al., 2022; Rose et al., 2022; Wang et al., 2022a; Wang et al., 2022b; Yu et al., 2023a; Yu et al., 2023b; Yu et al, 2023c). Beyond publications, MMLI is providing novel foundational AI advances through new information sources and new analytical techniques such as reaction aware multimodal molecular representation and a translator between natural language and molecules.…”

Section: Recent Research Accomplishmentsmentioning

confidence: 99%

Molecule Maker Lab Institute: Accelerating, advancing, and democratizing molecular innovation

Burke,

Denmark,

Diao

et al. 2024

AI Magazine

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Many of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI‐experts from the University of Illinois Urbana‐Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI‐enabled synthesis planning, (2) AI‐enabled catalyst development, (3) AI‐enabled molecule manufacturing, and (4) AI‐enabled molecule discovery. The MMLI's new AI‐enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use‐inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI‐enabled tools can help to make chemical synthesis accessible to nonexperts.

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