July 11, 2022 preprint Open access Machine learning approaches to quantitively predict selectivity of compounds against hDAC1 and hDAC6 isoforms bioRxiv (Cold Spring Harbor Laboratory) DOI: 10.1101/2022.07.10.499476 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 2 References 1 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Quantitative prediction of selectivity between the A1 and A2A adenosine receptors 2020 Journal of Cheminformatics article Computer Science Computational Drug Discovery Methods Open access 25 citations 1 result