ArticleJournal of cheminformatics2020
QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Interpretable Machine Learning Frameworks for QSAR Modeling: A Comparative Study of Classical Algorithms and Transformer-CNN Architectures.ACS omega · 2026Article
- Towards Explainable Computational Toxicology: Linking Antitargets to Rodent Acute Toxicity.Pharmaceutics · 2025Article
- Entangled fingerprints for quantum-encoded chemoinformatics: quantum circuits for molecular similarity in the noisy era.Frontiers in chemistry · 2025Article
- CPSign: conformal prediction for cheminformatics modeling.Journal of cheminformatics · 2024Article
- The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.Communications chemistry · 2024Article
- MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products.Journal of cheminformatics · 2024Article
- Applications and Potential of In Silico Approaches for Psychedelic Chemistry.Molecules (Basel, Switzerland) · 2023Review
- On the Best Way to Cluster NCI-60 Molecules.Biomolecules · 2023Article
- Review of the state of science and evaluation of currently available in silico prediction models for reproductive and developmental toxicity: A case study on pesticides.Birth defects research · 2022Article
- Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.Scientific reports · 2022Article
- Prediction of the Neurotoxic Potential of Chemicals Based on Modelling of Molecular Initiating Events Upstream of the Adverse Outcome Pathways of (Developmental) Neurotoxicity.International journal of molecular sciences · 2022Article
- Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.Journal of computer-aided molecular design · 2022Article
- Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification.Molecules (Basel, Switzerland) · 2021Article
- Harnessing In Silico, In Vitro, and In Vivo Data to Understand the Toxicity Landscape of Polycyclic Aromatic Compounds (PACs).Chemical research in toxicology · 2021Article
- Profiling and analysis of chemical compounds using pointwise mutual information.Journal of cheminformatics · 2021Article
- Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery.Molecular pharmaceutics · 2021Article
- From Big Data to Artificial Intelligence: chemoinformatics meets new challenges.Journal of cheminformatics · 2020Article
- QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction.Journal of cheminformatics · 2020Article
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8 authors.
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Abstract
An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.
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