Min Shen

Limitations of acyclovir and identification of potent HSV antivirals using 3D bioprinted human skin equivalents

The paper identifies potent HSV antivirals using a 3D bioprinted human skin equivalent platform and evaluates cell-type specific antiviral efficacy.

Identification of antibody-drug conjugate payloads which are substrates of ATP-binding cassette drug efflux transporters

This study identifies ADC payloads transported by ABC transporters, showing potential for drug resistance and suggesting payload selection to avoid this.

Discovery of Novel and Selective GPR17 Antagonists as Pharmacological Tools for Developing New Therapeutic Strategies in Diabetes and Obesity

The paper identifies two novel GPR17 antagonists through high-throughput screening that selectively inhibit GPR17 signaling and enhance GLP-1 secretion, offering potential for diabetes and obesity treatment.

Generation of a biliary tract cancer cell line atlas reveals molecular subtypes and therapeutic targets

This study creates a biliary tract cancer cell line atlas using multi-omics data to identify molecular subtypes and therapeutic targets.

A Comprehensive Collection of Pain and Opioid Use Disorder Compounds for High-Throughput Screening and Artificial Intelligence-Driven Drug Discovery

The paper creates a comprehensive small molecule library for pain and opioid use disorder with physical samples and an AI-driven platform for drug discovery.

A Graph Convolutional Network-based screening strategy for rapid identification of SARS-CoV-2 cell-entry inhibitors

The paper develops a graph convolutional network to screen compounds for SARS-CoV-2 entry inhibitors, identifying 16 active compounds from 170,000 virtual candidates.

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

MICHA is a pipeline that FAIRifies chemosensitivity assays by automatically annotating compounds, samples, reagents, and data processing methods, enabling interoperable and reusable drug screening data.

Machine learning on drug-specific data to predict small molecule teratogenicity

This paper uses machine learning to predict small molecule teratogenicity from structural and bioactivity data, identifying chemical features associated with teratogenic risk.