ArticlePhysical chemistry chemical physics : PCCP2022
Δ-Quantum machine-learning for medicinal chemistry.
Article in Physical chemistry chemical physics : PCCP, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Towards the design of artificial sensing materials via quantum-informed explainable AI.Journal of cheminformatics · 2026Article
- Predicting SJournal of chemical theory and computation · 2026Article
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Toward Improving Multiple Time Step QM/MM Simulations with Δ-Machine Learning.The journal of physical chemistry. B · 2025Article
- Studying Noncovalent Interactions in Molecular Systems with Machine Learning.Chemical reviews · 2025Review
- Quantum chemical properties of chlorinated polycyclic aromatic hydrocarbons for delta machine learning.Scientific data · 2025Article
- Article
- Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines.Digital discovery · 2025Article
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- G-Computational and structural biotechnology journal · 2024Article
- CombiningRSC advances · 2024Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 2024Article
- Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.Journal of chemical information and modeling · 2024Article
- Prospective de novo drug design with deep interactome learning.Nature communications · 2024Article
- Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning.Nature chemistry · 2024Article
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
- From intuition to AI: evolution of small molecule representations in drug discovery.Briefings in bioinformatics · 2023Article
- Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening.Communications chemistry · 2023Article
- Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.Molecules (Basel, Switzerland) · 2023Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.
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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.