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Physical Sciences and Mathematics Commons

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Life Sciences

City University of New York (CUNY)

Drug discovery

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Full-Text Articles in Physical Sciences and Mathematics

Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai Sep 2023

Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai

Dissertations, Theses, and Capstone Projects

Dark protein illumination is a fundamental challenge in drug discovery where majority human proteins are understudied, i.e. with only known protein sequence but no known small molecule binder. It's a major road block to enable drug discovery paradigm shift from single-targeted which looks to identify a single target and design drug to regulate the single target to multi-targeted in a Systems Pharmacology perspective. Diseases such as Alzheimer's and Opioid-Use-Disorder plaguing millions of patients call for effective multi-targeted approach involving dark proteins. Using limited protein data to predict dark protein property requires deep learning systems with OOD generalization capacity. Out-of-Distribution (OOD) …


Small Molecule Modulation Of Microbiota: A Systems Pharmacology Perspective, Qiao Liu, Bohyun Lee, Lei Xie Sep 2022

Small Molecule Modulation Of Microbiota: A Systems Pharmacology Perspective, Qiao Liu, Bohyun Lee, Lei Xie

Publications and Research

Background

Microbes are associated with many human diseases and influence drug efficacy. Small-molecule drugs may revolutionize biomedicine by fine-tuning the microbiota on the basis of individual patient microbiome signatures. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional “one-drug-one-target-one-disease” process. A systematic pharmacology approach that would suppress multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution.

Results

We construct a disease-centric signed microbe–microbe interaction network using curated microbe metabolite information and their effects on host. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of effect of microbes …


From Mollusks To Medicine: A Venomics Approach For The Discovery And Characterization Of Therapeutics From Terebridae Peptide Toxins, Aida Verdes, Prachi Anand, Juliette Gorson, Stephen Jannetti, Patrick Kelly, Abba Leffler, Danny Simpson, Girish Ramrattan, Mandë Holford Apr 2016

From Mollusks To Medicine: A Venomics Approach For The Discovery And Characterization Of Therapeutics From Terebridae Peptide Toxins, Aida Verdes, Prachi Anand, Juliette Gorson, Stephen Jannetti, Patrick Kelly, Abba Leffler, Danny Simpson, Girish Ramrattan, Mandë Holford

Publications and Research

Animal venoms comprise a diversity of peptide toxins that manipulate molecular targets such as ion channels and receptors, making venom peptides attractive candidates for the development of therapeutics to benefit human health. However, identifying bioactive venom peptides remains a significant challenge. In this review we describe our particular venomics strategy for the discovery, characterization, and optimization of Terebridae venom peptides, teretoxins. Our strategy reflects the scientific path from mollusks to medicine in an integrative sequential approach with the following steps: (1) delimitation of venomous Terebridae lineages through taxonomic and phylogenetic analyses; (2) identification and classification of putative teretoxins through omics …