AIMC Topic: Drug Discovery

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Revolutionizing pharmacokinetics: the dawn of AI-powered analysis.

Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques
This editorial explores how artificial intelligence (AI) is revolutionizing the science of pharmacokinetics (PK). It discusses the challenges of conventional PK analysis and how AI has transformed this area. It highlights the promise of artificial in...

Image2InChI: Automated Molecular Optical Image Recognition.

Journal of chemical information and modeling
The accurate identification and analysis of chemical structures in molecular images are prerequisites of artificial intelligence for drug discovery. It is important to efficiently and automatically convert molecular images into machine-readable repre...

Deep reinforcement learning enables better bias control in benchmark for virtual screening.

Computers in biology and medicine
Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-th...

Artificial intelligence for drug discovery and development in Alzheimer's disease.

Current opinion in structural biology
The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics...

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery.

Expert opinion on drug discovery
INTRODUCTION: Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted...

Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases.

Pharmaceutical medicine
The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a ...

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated proce...

Benford's Law and distributions for better drug design.

Expert opinion on drug discovery
INTRODUCTION: Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and ...

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.

Journal of chemical information and modeling
Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in o...

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Journal of chemical information and modeling
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a ...