ArticleJournal of chemical theory and computation2022
Chemical Space Exploration with Active Learning and Alchemical Free Energies.
Article in Journal of chemical theory and computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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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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.
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Who cites it
39 citing papers in PubMed, 88 citations in OpenAlex.
- Active learning in pharmaceutical 3D printing: a multi-dataset comparison.Drug delivery and translational research · 2026Article
- Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026Article
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Scaffold-based evaluation metrics for fair comparison of molecular generators.Journal of cheminformatics · 2026Article
- Free energy calculations in molecular modeling: from classical methods to machine learning.Journal of molecular modeling · 2026Review
- Query Matters: How Selection Strategies Influence Active Learning in Drug Discovery.Journal of chemical information and modeling · 2026Article
- A simple compound prioritization method for drug discovery considering multi-target binding.Digital discovery · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- Automated navigation of condensate phase behavior with active machine learning.Nature communications · 2025Article
- Integrating Machine Learning into Free Energy Perturbation Workflows.Journal of chemical information and modeling · 2025Review
- How many crystal structures do you need to trust your docking results?bioRxiv : the preprint server for biology · 2025Article
- Scaffold Hopping with Generative Reinforcement Learning.Journal of chemical information and modeling · 2025Article
- PAL - parallel active learning for machine-learned potentials.Digital discovery · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Active Learning Improves Ionization Efficiency Predictions and Quantification in Nontargeted LC/HRMS.Analytical chemistry · 2025Article
- Predicting high-fitness viral protein variants with Bayesian active learning and biophysics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Active Learning FEP Using 3D-QSAR for Prioritizing Bioisosteres in Medicinal Chemistry.ACS medicinal chemistry letters · 2025Article
- Article
- Automated On-the-Fly Optimization of Resource Allocation for Efficient Free Energy Simulations.Journal of chemical information and modeling · 2025Article
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
Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate toward potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds, high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives.
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What OpenQuestion holds
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.