Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

Akshat Kumar Nigam, Robert Pollice*, Gary Tom, Kjell Jorner, John Willes, Luca Thiede, Anshul Kundaje, Alán Aspuru-Guzik

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)

    Abstract

    The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelligence development, many algorithms have been developed to tackle this problem. However, despite the emergence of many new approaches in recent years, comparatively little progress has been made in developing realistic benchmarks that reflect the complexity of molecular design for real-world applications. In this work, we develop a set of practical benchmark tasks relying on physical simulation of molecular systems mimicking real-life molecular design problems for materials, drugs, and chemical reactions. Additionally, we demonstrate the utility and ease of use of our new benchmark set by demonstrating how to compare the performance of several well-established families of algorithms. Surprisingly, we find that model performance can strongly depend on the benchmark domain. We believe that our benchmark suite will help move the field towards more realistic molecular design benchmarks, and move the development of inverse molecular design algorithms closer to designing molecules that solve existing problems in both academia and industry alike.

    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems
    EditorsA. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, S. Levine
    PublisherCurran Associates Inc
    Pages3263-3306
    Number of pages44
    Volume36
    Publication statusPublished - 2023
    Event37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, United States
    Duration: 10-Dec-202316-Dec-2023

    Publication series

    NameAdvances in Neural Information Processing Systems
    ISSN (Print)1049-5258

    Conference

    Conference37th Conference on Neural Information Processing Systems, NeurIPS 2023
    Country/TerritoryUnited States
    CityNew Orleans
    Period10/12/202316/12/2023

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