Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials.

John, Lijo; Mahanta, Hridoy Jyoti; Soujanya, Y; Sastry, G Narahari

John, Lijo; Mahanta, Hridoy Jyoti; Soujanya, Y; Sastry, G Narahari.

Afiliação

John L; Advanced Computation and Data Sciences Division, CSIR- North East Institute of Science and Technology, Jorhat, 785006, Assam, India; Polymers and Functional Materials Division, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India; Academy of Scientific and Innovative Research (AcSI
Mahanta HJ; Advanced Computation and Data Sciences Division, CSIR- North East Institute of Science and Technology, Jorhat, 785006, Assam, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India.
Soujanya Y; Polymers and Functional Materials Division, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India.
Sastry GN; Advanced Computation and Data Sciences Division, CSIR- North East Institute of Science and Technology, Jorhat, 785006, Assam, India; Polymers and Functional Materials Division, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India; Academy of Scientific and Innovative Research (AcSI

Comput Biol Med ; 153: 106494, 2023 02.

Article em En | MEDLINE | ID: mdl-36587568

Assuntos

Drogas em Investigação; Aprendizado de Máquina; Drogas em Investigação/uso terapêutico

Palavras-chave

Clinical trials; Feature selection; Fingerprints; Machine learning; Molecular descriptors

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Drogas em Investigação / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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