Journal article
Machine Learning and Autonomous Systems for Accelerated Synthesis
Annual review of analytical chemistry (Palo Alto, Calif.), v 19(1), pp 331-353
10 Feb 2026
PMID: 41666041
Abstract
Autonomous systems integrating machine learning (ML) and laboratory automation are transforming synthetic chemistry by enabling closed-loop experimentation and discovery. In this review, we examine the state-of-the-art in autonomous systems for organic synthesis, with a focus on the components, configurations, and ML algorithms that enable automated reaction planning, execution, and optimization. We survey representative systems that span applications from reaction discovery to molecular optimization, comparing flow and batch configurations and identifying trends in system design. Emphasis is placed on the critical bottlenecks of purification and analytical measurement, particularly structural elucidation of unexpected products-areas that currently constrain autonomous platforms. We describe recent advances in chromatographic method development, structural elucidation from mass spectrometry and nuclear magnetic resonance, and novel ML-based approaches to quantify complex mixtures without calibration. By focusing on enabling technologies in chemical analysis, we identify opportunities for ML and automation to expand beyond domain-specific platforms and accelerate the pace of synthetic discovery.
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Details
- Title
- Machine Learning and Autonomous Systems for Accelerated Synthesis
- Creators
- Matthew A McDonald - Drexel UniversityKlavs F Jensen - Massachusetts Institute of Technology
- Publication Details
- Annual review of analytical chemistry (Palo Alto, Calif.), v 19(1), pp 331-353
- Publisher
- Annual Reviews
- Number of pages
- 23
- Grant note
- DARPA Accelerated Molecular Discovery program: HR00111920025 MIT Consortium Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS)
The authors would like to thank the DARPA Accelerated Molecular Discovery program (under contract HR00111920025) and the MIT Consortium Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) for support leading to this review.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:001767678300007
- Scopus ID
- 2-s2.0-105039292516
- Other Identifier
- 991022163437104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Chemistry, Analytical
- Spectroscopy