ReviewJournal of chemical information and modeling2024
When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges.
Review in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
The trial behind it
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Who cites it
18 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Yield Smarter, Not Harder: Good Practices for Machine Learning of Reaction Outcomes.Journal of the American Chemical Society · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Infrared Spectral Descriptors for Reaction Yield Prediction: Toward Redefining Experimental Spaces.Molecular informatics · 2026Article
- Regularized regression in ultra-small chemometric datasets: A methodological case study using FTIR spectra of Schiff bases.PloS one · 2026Article
- Using Yield Profiles to Study Reaction Mechanism.ACS catalysis · 2025Article
- Automating structure-activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations.National science review · 2025Article
- Reaction-conditioned generative model for catalyst design and optimization with CatDRX.Communications chemistry · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry.ACS engineering Au · 2025Article
- Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides.Journal of the American Chemical Society · 2025Article
- An active representation learning method for reaction yield prediction with small-scale data.Communications chemistry · 2025Article
- Suitability of large language models for extraction of high-quality chemical reaction dataset from patent literature.Journal of cheminformatics · 2024Article
- Article
- Developing BioNavi for Hybrid Retrosynthesis Planning.JACS Au · 2024Article
- Deep Kernel learning for reaction outcome prediction and optimization.Communications chemistry · 2024Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.
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Registered trials
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.