Next Article in Journal
Phenolic Compounds in Organic and Aqueous Extracts from Acacia farnesiana Pods Analyzed by ULPS-ESI-Q-oa/TOF-MS. In Vitro Antioxidant Activity and Anti-Inflammatory Response in CD-1 Mice
Previous Article in Journal
Surface Hydrophobicity and Functional Properties of Citric Acid Cross-Linked Whey Protein Isolate: The Impact of pH and Concentration of Citric Acid
Previous Article in Special Issue
Enhancing the Delivery of Chemotherapeutics: Role of Biodegradable Polymeric Nanoparticles
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 23
Issue 9
10.3390/molecules23092384
molecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Deep Learning in Drug Discovery and Medicine; Scratching the Surface

by
Dibyendu Dana
1,†,
Satishkumar V. Gadhiya
1,†,
Luce G. St. Surin
1,†,
David Li
1,
Farha Naaz
1,
Quaisar Ali
1,
Latha Paka
1,
Michael A. Yamin
1,
Mahesh Narayan
2,
Itzhak D. Goldberg
1 and
Prakash Narayan
1,*
1
Department of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USA
2
Department of Chemistry and Biochemistry, The University of Texas, El Paso, TX 79968, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Molecules 2018, 23(9), 2384; https://doi.org/10.3390/molecules23092384
Submission received: 10 July 2018 / Revised: 6 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
(This article belongs to the Special Issue Directed Drug Design and Molecular Therapy)
Browse Figures
Versions Notes

Abstract

The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy. Indeed, precision medicine-based therapies that first found their place in oncology are rapidly finding uses in autoimmune, renal and other diseases. More recently a new renaissance that is shaping everyday life is making its way into healthcare. Drug discovery and medicine that started with Ayurveda in India are now benefiting from an altogether different artificial intelligence (AI)—one which is automating the invention of new chemical entities and the mining of large databases in health-privacy-protected vaults. Indeed, disciplines as diverse as language, neurophysiology, chemistry, toxicology, biostatistics, medicine and computing have come together to harness algorithms based on transfer learning and recurrent neural networks to design novel drug candidates, a priori inform on their safety, metabolism and clearance, and engineer their delivery but only on demand, all the while cataloging and comparing omics signatures across traditionally classified diseases to enable basket treatment strategies. This review highlights inroads made and being made in directed-drug design and molecular therapy.
Keywords: drug discovery; therapeutics; small molecules; precision medicine; artificial intelligence; deep learning; transfer learning; recurrent neural networks; de novo design drug discovery; therapeutics; small molecules; precision medicine; artificial intelligence; deep learning; transfer learning; recurrent neural networks; de novo design
Graphical Abstract

Share and Cite

MDPI and ACS Style

Dana, D.; Gadhiya, S.V.; St. Surin, L.G.; Li, D.; Naaz, F.; Ali, Q.; Paka, L.; Yamin, M.A.; Narayan, M.; Goldberg, I.D.; et al. Deep Learning in Drug Discovery and Medicine; Scratching the Surface. Molecules 2018, 23, 2384. https://doi.org/10.3390/molecules23092384

AMA Style

Dana D, Gadhiya SV, St. Surin LG, Li D, Naaz F, Ali Q, Paka L, Yamin MA, Narayan M, Goldberg ID, et al. Deep Learning in Drug Discovery and Medicine; Scratching the Surface. Molecules. 2018; 23(9):2384. https://doi.org/10.3390/molecules23092384

Chicago/Turabian Style

Dana, Dibyendu, Satishkumar V. Gadhiya, Luce G. St. Surin, David Li, Farha Naaz, Quaisar Ali, Latha Paka, Michael A. Yamin, Mahesh Narayan, Itzhak D. Goldberg, and et al. 2018. "Deep Learning in Drug Discovery and Medicine; Scratching the Surface" Molecules 23, no. 9: 2384. https://doi.org/10.3390/molecules23092384

APA Style

Dana, D., Gadhiya, S. V., St. Surin, L. G., Li, D., Naaz, F., Ali, Q., Paka, L., Yamin, M. A., Narayan, M., Goldberg, I. D., & Narayan, P. (2018). Deep Learning in Drug Discovery and Medicine; Scratching the Surface. Molecules, 23(9), 2384. https://doi.org/10.3390/molecules23092384

Article Metrics

Back to TopTop