AIMC Topic: Drug Discovery

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How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons.

Drug discovery today
AI techniques are making inroads into the field of drug discovery. As a result, a growing number of drugs and vaccines have been discovered using AI. However, questions remain about the success of these molecules in clinical trials. To address these ...

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells.

Science (New York, N.Y.)
Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advan...

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches.

Drug discovery today
Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-β-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell...

Deep learning for low-data drug discovery: Hurdles and opportunities.

Current opinion in structural biology
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such ...

From GPUs to AI and quantum: three waves of acceleration in bioinformatics.

Drug discovery today
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel acceler...

Unleashing the power of generative AI in drug discovery.

Drug discovery today
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de...

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology.

Journal of chemical information and modeling
In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machin...

Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: Molecular Generation Based on a Low Activity Lead Compound.

Journal of medicinal chemistry
Artificial intelligence (AI) molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models emerging as powerful tools. This study introduces a fragment-by-fragment growth RL forward molecular...

[The revolution of AI in drug development].

Medecine sciences : M/S
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to repre...