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QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping.

, , , , , , , and . J. Cheminformatics, 12 (1): 39 (2020)

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Computational Approaches for De Novo Drug Design: Past, Present, and Future., , and . Artificial Neural Networks, 3rd Edition, volume 2190 of Methods in Molecular Biology, Springer, (2021)Significantly Improved HIV Inhibitor Efficacy Prediction Employing Proteochemometric Models Generated From Antivirogram Data., , , , , and . PLoS Comput. Biol., (2013)Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets., , , , , and . J. Cheminformatics, (2013)Papyrus: a large-scale curated dataset aimed at bioactivity predictions., , , , , and . J. Cheminformatics, 15 (1): 3 (2023)DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning., , , , and . J. Cheminformatics, 15 (1): 24 (December 2023)An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor., , , , and . J. Cheminformatics, 11 (1): 35:1-35:16 (2019)QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping., , , , , , , and . J. Cheminformatics, 12 (1): 39 (2020)Proteochemometric modeling in a Bayesian framework., , , , , and . J. Cheminformatics, 6 (1): 35 (2014)Chemogenomics: Looking at biology through the lens of chemistry., , , , , , , , and . Stat. Anal. Data Min., 2 (3): 149-160 (2009)Chemical, Target, and Bioactive Properties of Allosteric Modulation., , and . PLoS Comput. Biol., (2014)