Evidence map›Paper›PMID 40397546›Full record

ArticleAccounts of chemical research2025

ASKCOS: Open-Source, Data-Driven Synthesis Planning.

Zhengkai Tu, Sourabh J Choure, Mun Hong Fong, Jihye Roh, Itai Levin, Kevin Yu, Joonyoung F Joung, Nathan Morgan, Shih-Cheng Li, Xiaoqi Sun and 9 more

Abstract read
In one paragraph

Article in Accounts of chemical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

  1. Article
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  6. AI-Based Retrosynthesis of Known and Unexplored Covalent Organic Framework Monomers.Chemistry of materials : a publication of the American Chemical Society · 2026
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  9. Article
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  17. Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Zhengkai TuDepartment of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts 02139, United States.
Sourabh J ChoureDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Mun Hong FongDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Jihye RohDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.ORCID 0009-0005-0123-0646
Itai LevinDepartment of Biological Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Kevin YuCenter for Computational Science and Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Joonyoung F JoungDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Nathan MorganDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Shih-Cheng LiDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.ORCID 0000-0001-8645-0034
Xiaoqi SunDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Huiqian LinDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Mark MurninDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Jordan P LilesDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Thomas J StrubleBristol Myers Squibb, Cambridge, Massachusetts 02142, United States.ORCID 0000-0003-1695-2367
Michael E FortunatoNovartis Institutes for BioMedical Research, Inc., Cambridge, Massachusetts 02139, United States.
Mengjie LiuDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.ORCID 0000-0003-2414-1986
William H GreenDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.ORCID 0000-0003-2603-9694
Klavs F JensenDepartment of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.ORCID 0000-0001-7192-580X
Connor W ColeyDepartment of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts 02139, United States.ORCID 0000-0002-8271-8723

Funding

Informatics and Machine Learning Modules for Research Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous LaboratoryU18TR004149 · NCATS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI COLEY, CONNOR WILSON · 2022 to 2023
$1.1M
NCATS NIH HHS U18 TR004149
6 · The paper itself

Abstract

ConspectusThe advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. In this Account, we describe the range of data-driven methods and models that have been incorporated into the newest version of ASKCOS, an open-source software suite for synthesis planning that we have been developing since 2016. This ongoing effort has been driven by the importance of bridging the gap between research and development, making research advances available through a freely available practical tool. ASKCOS integrates modules for retrosynthetic planning, modules for complementary capabilities of condition prediction and reaction product prediction, and several supplementary modules and utilities with various roles in synthesis planning. For retrosynthetic planning, we have developed an Interactive Path Planner (IPP) for user-guided search as well as a Tree Builder for automatic planning with two well-known tree search algorithms, Monte Carlo Tree Search (MCTS) and Retro*. Four one-step retrosynthesis models covering template-based and template-free strategies form the basis of retrosynthetic predictions and can be used simultaneously to combine their advantages and propose diverse suggestions. Strategies for assessing the feasibility of proposed reaction steps and evaluating the full pathways are built on top of several pioneering efforts that we have made in the subtasks of reaction condition recommendation, pathway scoring and clustering, and the prediction of reaction outcomes including the major product, impurities, site selectivity, and regioselectivity. In addition, we have also developed auxiliary capabilities in ASKCOS based on our past and ongoing work for solubility prediction and quantum mechanical descriptor prediction, which can provide more insight into the suitability of proposed reaction solvents or the hypothetical selectivity of desired transformations. For each of these capabilities, we highlight its relevance in the context of synthesis planning and present a comprehensive overview of how it is built on top of not only our work but also of other recent advancements in the field. We also describe in detail how chemists can easily interact with these capabilities via user-friendly interfaces. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks by complementing expert decision making and route ideation. It is our belief that CASP tools are an important part of modern chemistry research and offer ever-increasing utility and accessibility.

Identifiers

PMID40397546
PMCPMC12140107

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.