ArticleJournal of computer-aided molecular design2022
A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.
Article in Journal of computer-aided molecular design, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Predicting Ligand Binding Modes by Scaffold-Guided Structure Refinement.Journal of medicinal chemistry · 2026Article
- Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026Article
- FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction.Nature communications · 2026Article
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Hierarchical affinity landscape navigation through learning a shared pocket-ligand space.Patterns (New York, N.Y.) · 2025Article
- Evolution of macromolecular crystallography beamlines at the Swiss Light Source and SwissFEL.Journal of synchrotron radiation · 2025Article
- A high-resolution data set of fatty acid-binding protein structures. III. Unexpectedly high occurrence of wrong ligands.Acta crystallographica. Section D, Structural biology · 2025Article
- A high-resolution data set of fatty acid-binding protein structures. II. Crystallographic overview, ligand classes and binding pose.Acta crystallographica. Section D, Structural biology · 2025Article
- ProteinsPlus: a publicly available resource for protein structure mining.Nucleic acids research · 2025Article
- The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks.Journal of chemical information and modeling · 2025Review
- Benchmarking the robustness of the correct identification of flexible 3D objects using common machine learning models.Patterns (New York, N.Y.) · 2025Article
- Article
- CombiningRSC advances · 2024Article
- From mundane to surprising nonadditivity: drivers and impact on ML models.Journal of computer-aided molecular design · 2024Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 2024Article
- Benchmarking compound activity prediction for real-world drug discovery applications.Communications chemistry · 2024Article
- Prospective de novo drug design with deep interactome learning.Nature communications · 2024Article
- Combining ICJournal of chemical information and modeling · 2024Article
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
- The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.International journal of molecular sciences · 2023Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.
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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.