Deep Learning in Drug Discovery and Medicine; Scratching the Surface
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 inva...
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doaj-131e90281dfd48e48d32a239ad8862362020-11-25T00:40:21ZengMDPI AGMolecules1420-30492018-09-01239238410.3390/molecules23092384molecules23092384Deep Learning in Drug Discovery and Medicine; Scratching the SurfaceDibyendu Dana0Satishkumar V. Gadhiya1Luce G. St. Surin2David Li3Farha Naaz4Quaisar Ali5Latha Paka6Michael A. Yamin7Mahesh Narayan8Itzhak D. Goldberg9Prakash Narayan10Department of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Chemistry and Biochemistry, The University of Texas, El Paso, TX 79968, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USADepartment of Preclinical Research, Angion Biomedica Corporation, Nassau, NY 11553, USAThe 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.http://www.mdpi.com/1420-3049/23/9/2384drug discoverytherapeuticssmall moleculesprecision medicineartificial intelligencedeep learningtransfer learningrecurrent neural networksde novo design |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dibyendu Dana Satishkumar V. Gadhiya Luce G. St. Surin David Li Farha Naaz Quaisar Ali Latha Paka Michael A. Yamin Mahesh Narayan Itzhak D. Goldberg Prakash Narayan |
spellingShingle |
Dibyendu Dana Satishkumar V. Gadhiya Luce G. St. Surin David Li Farha Naaz Quaisar Ali Latha Paka Michael A. Yamin Mahesh Narayan Itzhak D. Goldberg Prakash Narayan Deep Learning in Drug Discovery and Medicine; Scratching the Surface Molecules drug discovery therapeutics small molecules precision medicine artificial intelligence deep learning transfer learning recurrent neural networks de novo design |
author_facet |
Dibyendu Dana Satishkumar V. Gadhiya Luce G. St. Surin David Li Farha Naaz Quaisar Ali Latha Paka Michael A. Yamin Mahesh Narayan Itzhak D. Goldberg Prakash Narayan |
author_sort |
Dibyendu Dana |
title |
Deep Learning in Drug Discovery and Medicine; Scratching the Surface |
title_short |
Deep Learning in Drug Discovery and Medicine; Scratching the Surface |
title_full |
Deep Learning in Drug Discovery and Medicine; Scratching the Surface |
title_fullStr |
Deep Learning in Drug Discovery and Medicine; Scratching the Surface |
title_full_unstemmed |
Deep Learning in Drug Discovery and Medicine; Scratching the Surface |
title_sort |
deep learning in drug discovery and medicine; scratching the surface |
publisher |
MDPI AG |
series |
Molecules |
issn |
1420-3049 |
publishDate |
2018-09-01 |
description |
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. |
topic |
drug discovery therapeutics small molecules precision medicine artificial intelligence deep learning transfer learning recurrent neural networks de novo design |
url |
http://www.mdpi.com/1420-3049/23/9/2384 |
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