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Drug Design

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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...

Structure-based drug design with geometric deep learning.

Current opinion in structural biology
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has be...

Artificial intelligence in multi-objective drug design.

Current opinion in structural biology
The factors determining a drug's success are manifold, making de novo drug design an inherently multi-objective optimisation (MOO) problem. With the advent of machine learning and optimisation methods, the field of multi-objective compound design has...

New avenues in artificial-intelligence-assisted drug discovery.

Drug discovery today
Over the past decade, the amount of biomedical data available has grown at unprecedented rates. Increased automation technology and larger data volumes have encouraged the use of machine learning (ML) or artificial intelligence (AI) techniques for mi...

Chemistry42: An AI-Driven Platform for Molecular Design and Optimization.

Journal of chemical information and modeling
Chemistry42 is a software platform for small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structu...