AI Medical Compendium Topic

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Drug Evaluation, Preclinical

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Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.

Future medicinal chemistry
Immune checkpoint inhibitors targeting PD-L1 are crucial in cancer research for preventing cancer cells from evading the immune system. This study developed a screening model combining ANN, molecular similarity, and GNINA 1.0 docking to target PD-L1...

Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation.

Future medicinal chemistry
Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million co...

Combined structure-based virtual screening and machine learning approach for the identification of potential dual inhibitors of ACC and DGAT2.

International journal of biological macromolecules
Acetyl-coenzyme A carboxylase (ACC) and diacylglycerol acyltransferase 2 (DGAT2) are recognized as potential therapeutic targets for nonalcoholic fatty liver disease (NAFLD). Inhibitors targeting ACC and DGAT2 have exhibited the capacity to reduce he...

In silico method and bioactivity evaluation to discover novel antimicrobial agents targeting FtsZ protein: Machine learning, virtual screening and antibacterial mechanism study.

Naunyn-Schmiedeberg's archives of pharmacology
This research paper utilizes a fused-in-silico approach alongside bioactivity evaluation to identify active FtsZ inhibitors for drug discovery. Initially, ROC-guided machine learning was employed to obtain almost 13182 compounds from three libraries....

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.

Journal of chemical information and modeling
Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is time-consuming and almost infeasible to screen an immense number of compounds for anticancer activit...

E-pharmacophore and deep learning based high throughput virtual screening for identification of CDPK1 inhibitors of Cryptosporidium parvum.

Computational biology and chemistry
Cryptosporidiosis, a prevalent gastrointestinal illness worldwide, is caused by the protozoan parasite Cryptosporidium parvum. Calcium-dependent protein kinase 1 (CpCDPK1), crucial for the parasite's life cycle, serves as a promising drug target due ...

FitScore: a fast machine learning-based score for 3D virtual screening enrichment.

Journal of computer-aided molecular design
Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large d...

An artificial intelligence accelerated virtual screening platform for drug discovery.

Nature communications
Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding p...

Machine Learning-Assisted High-Throughput Screening for Anti-MRSA Compounds.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening.

Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.

Annual review of pharmacology and toxicology
In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 he...