AIMC Topic: Drug Design

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TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning.

Journal of chemical information and modeling
One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based drug design, which we call TRIOMPHE (t...

Comprehensive Survey of Recent Drug Discovery Using Deep Learning.

International journal of molecular sciences
Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related...

Accelerating antibiotic discovery through artificial intelligence.

Communications biology
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguish...

From computer-aided drug discovery to computer-driven drug discovery.

Drug discovery today. Technologies
Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discovery process, but were not commonly regarded as a driving force in small molecule drug discovery. In t...

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.

Molecular diversity
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The ava...

AI in drug development: a multidisciplinary perspective.

Molecular diversity
The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this...

Structure-Based Molecular Generator Combined with Artificial Intelligence and Docking Simulations.

Journal of chemical information and modeling
Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have serious limitations in the context of drug design wherein they do not suffici...

Artificial intelligence and the future of life sciences.

Drug discovery today
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials;...

Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.

Molecular diversity
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating disea...