AIMC Topic: Drug Discovery

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Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure-Activity Relationship Models.

Chemical research in toxicology
The search for chemical hit material is a lengthy and increasingly expensive drug discovery process. To improve it, ligand-based quantitative structure-activity relationship models have been broadly applied to optimize primary and secondary compound ...

A Simple Way to Incorporate Target Structural Information in Molecular Generative Models.

Journal of chemical information and modeling
Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. Th...

AI/ML in Precision Medicine: A Look Beyond the Hype.

Therapeutic innovation & regulatory science
Artificial Intelligence (AI) and Machine Learning (ML) are making headlines in medical research, especially in drug discovery, digital imaging, disease diagnostics, genetic testing, and optimal care pathway (personalized care). However, the potential...

Structure-Based Drug Discovery with Deep Learning.

Chembiochem : a European journal of chemical biology
Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of th...

Prediction of Human Organ Toxicity via Artificial Intelligence Methods.

Chemical research in toxicology
Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strategies in the early stages of drug development for human toxicity assessment. At present, artificial in...

Deep learning-based classification model for GPR151 activator activity prediction.

BMC bioinformatics
BACKGROUND: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of met...

Artificial Intelligence for Computer-Aided Drug Discovery.

Drug research
The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribut...

BRMCF: Binary Relevance and MLSMOTE Based Computational Framework to Predict Drug Functions From Chemical and Biological Properties of Drugs.

IEEE/ACM transactions on computational biology and bioinformatics
In silico machine learning based prediction of drug functions considering the drug properties would substantially enhance the speed and reduce the cost of identifying promising drug leads. The drug function prediction capability of different drug pro...

Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery.

Chemical communications (Cambridge, England)
We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demo...

Sulfur-containing marine natural products as leads for drug discovery and development.

Current opinion in chemical biology
Among the large series of marine natural products (MNPs), sulfur-containing MNPs have emerged as potential therapeutic agents for the treatment of a range of diseases. Herein, we reviewed 95 new sulfur-containing MNPs isolated during the period betwe...