Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques-Reference-Cited by-同舟云学术

Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques

Published:2023-12-12 Issue:50 Volume:127 Page:10691-10699
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Provasi Davide¹^ORCID,Filizola Marta¹^ORCID

Affiliation:

1. Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, New York 10029, United States

Funder

National Center for Advancing Translational Sciences

Office of Research Infrastructure Programs, National Institutes of Health

National Institute on Drug Abuse

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Surfaces, Coatings and Films,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.3c05306

Reference39 articles.

1. NIDA’s medication development priorities in response to the Opioid Crisis: ten most wanted

2. Current strategies toward safer mu opioid receptor drugs for pain management

3. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

4. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

5. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

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