Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2209.12487 v4 pith:2BJSR6I6 submitted 2022-09-26 cs.CE

classification cs.CE
keywords designmolecularbenchmarkalgorithmsrealisticbeenbenchmarkschemical
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Active Learning Enables Extrapolation in Molecular Generative Models

    cs.LG 2025-01 conditional novelty 6.0 of 10

    Retraining molecular property predictors on active-learning batches of DFT-computed molecules enables a genetic generative model to generate molecules with properties beyond the training data and more stable molecules.

  2. Balancing property optimization and constraint satisfaction for constrained multi-property molecular optimization

    physics.chem-ph 2024-11 conditional novelty 5.0 of 10

    CMOMO is an evolutionary framework that balances multi-property optimization with constraint satisfaction in latent molecular space and outperforms five baselines on four tasks.

Pith tools