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Skip to content
  • About
    • Overview
    • Partner Institutions
    • Our Facilities
    • Job Openings
    • Opportunities for MMLI Trainees
  • People
    • Overview
    • Leadership
    • External Advisory Board
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  • Research
    • Overview
    • Development of Foundational AI Agents
    • Catalyst Discovery
    • Drug Discovery
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  • MATRIX Program
    • Overview
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    • MMLI Fellowship Program
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Archives: Data Sets

L+M-24: Building a Dataset for Language+Molecules @ ACL 2024

High-Level Data Fusion Enables the Chemoinformatically Guided Discovery of Chiral Disulfonimide Catalysts for Atropselective Iodination of 2-Amino-6-arylpyridines

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling

A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings

Enzyme function prediction using contrastive learning

Calibrated geometric deep learning improves kinase–drug binding predictions

Machine Learning to Develop Peptide Catalysts –Successes, Limitations and Opportunities

Chemoinformatic Catalyst Selection Methods for the Optimization of Copper–Bis(oxazoline)-Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions

Closed-Loop Transfer Enables AI to Yield Chemical Knowledge

Interplay of Spatial and Topological Defects in Polymer Networks

Contribution of Signaling Partner Association to Strigolactone Receptor Selectivity

Control of Lithium Salt Partitioning, Coordination, and Solvation in Vitrimer Electrolytes

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    The Molecule Maker Lab Institute is an AI Institute for Molecular Discovery, Synthesis Strategy, and Manufacturing supported by the U.S. National Science Foundation under Award No. 2019897 and 2505932. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.