Pith. sign in

REVIEW 1 cited by

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities

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 2502.17456 v1 pith:G6IHY3RI submitted 2025-02-09 physics.chem-ph cs.AIcs.LG

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities

classification physics.chem-ph cs.AIcs.LG
keywords methodsvariouschemistryreviewtechniquesaccelerateacrossai-assisted
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms. In particular, the use of AI-assisted methods to accelerate chemical research has become a popular and rapidly growing trend, leading to numerous groundbreaking works. In this paper, we provide a comprehensive review of current AI techniques in chemistry from a computational perspective, considering various aspects in the design of methods. We begin by discussing the characteristics of data from diverse sources, followed by an overview of various representation methods. Next, we review existing models for several topical tasks in the field, and conclude by highlighting some key challenges that warrant further attention.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

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

  1. MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing

    cs.AI 2026-05 unverdicted novelty 6.0

    MACReD is a multi-agent collaborative reasoning framework for reaction diagram parsing that reports state-of-the-art F1 scores of 75.2% and 84.6% on the RxnScribe benchmark.