AIMC Topic: Drug Design

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Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis.

Journal of biomolecular structure & dynamics
The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the...

Artifical intelligence: a virtual chemist for natural product drug discovery.

Journal of biomolecular structure & dynamics
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to...

Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration.

BMC bioinformatics
The flourishment of machine learning and deep learning methods has boosted the development of cheminformatics, especially regarding the application of drug discovery and new material exploration. Lower time and space expenses make it possible for sci...

De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning.

Journal of molecular modeling
CONTEXT: In recent decades, drug development has become extremely important as different new diseases have emerged. However, drug discovery is a long and complex process with a very low success rate, and methods are needed to improve the efficiency o...

A compact review of progress and prospects of deep learning in drug discovery.

Journal of molecular modeling
BACKGROUND: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer...

Artificial intelligence in molecular de novo design: Integration with experiment.

Current opinion in structural biology
In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progr...

Can We Quickly Learn to "Translate" Bioactive Molecules with Transformer Models?

Journal of chemical information and modeling
Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a t...

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.

Drug discovery today
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrate...

How can machine learning and multiscale modeling benefit ocular drug development?

Advanced drug delivery reviews
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug del...

On the Best Way to Cluster NCI-60 Molecules.

Biomolecules
Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. Howe...