AIMC Topic: Drug Design

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Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

Computational and mathematical methods in medicine
A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inh...

Prediction of activity cliffs on the basis of images using convolutional neural networks.

Journal of computer-aided molecular design
An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure-activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein...

The challenges of generalizability in artificial intelligence for ADME/Tox endpoint and activity prediction.

Expert opinion on drug discovery
INTRODUCTION: Artificial intelligence (AI) has seen a massive resurgence in recent years with wide successes in computer vision, natural language processing, and games. The similar creation of robust and accurate AI models for ADME/Tox endpoint and a...

GGL-Tox: Geometric Graph Learning for Toxicity Prediction.

Journal of chemical information and modeling
Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large libr...

Target2DeNovoDrug: a novel programmatic tool for -deep learning based drug design for any target of interest.

Journal of biomolecular structure & dynamics
The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates and de...

A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Molecular diversity
Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potentia...

Trends in application of advancing computational approaches in GPCR ligand discovery.

Experimental biology and medicine (Maywood, N.J.)
G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefo...

The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design.

Life sciences
Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery s...

Prioritizing Pain-Associated Targets with Machine Learning.

Biochemistry
While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model ...