ArticleNature communications2023
Extracting medicinal chemistry intuition via preference machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
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- A multi-fidelity tabular prior-data fitted network model for accurate prediction and uncertainty quantification.Nature communications · 2026Article
- Bridging antiviral drug discovery with a large language model-powered framework.Communications biology · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- Scaffold Hopping with Generative Reinforcement Learning.Journal of chemical information and modeling · 2025Article
- A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists.Nature chemistry · 2025Article
- Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations.Journal of cheminformatics · 2025Article
- E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays.Journal of cheminformatics · 2025Article
- Development and experimental validation of a machine learning model for the prediction of new antimalarials.BMC chemistry · 2025Article
- Metis: a python-based user interface to collect expert feedback for generative chemistry models.Journal of cheminformatics · 2024Article
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
- Extracting medicinal chemistry intuition via preference machine learning.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist's career. In this work we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased de novo drug design. Annotated response data is provided, and developed models and code made available through a permissive open-source license.
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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.