AIMC Topic: Drug Discovery

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AI in drug discovery: A wake-up call.

Drug discovery today
Following a proof-of-concept presentation on dual-use artificial intelligence (AI) in drug discovery by Collaborations Pharmaceuticals Inc. to the Swiss Federal Institute for NBC-Protection, we explored how a generative algorithm could develop the ne...

Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery?

Expert opinion on drug discovery
INTRODUCTION: Deep learning approaches have become popular in recent years in de novo drug design. Generative models for molecule generation and optimization have shown promising results. Molecules trained on different chemical data could regenerate ...

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.

International journal of molecular sciences
Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) i...

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.

CPT: pharmacometrics & systems pharmacology
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific dat...

DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model.

IEEE/ACM transactions on computational biology and bioinformatics
Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies ...

Advancing health care via artificial intelligence: From concept to clinic.

European journal of pharmacology
Ever Since, pharmaceutical companies are facing challenges to develop new drug products faster and economical with good quality, safety and efficacy. The advent of Artificial intelligence (AI) with computational technology empowers scientists, impact...

3DMol-Net: Learn 3D Molecular Representation Using Adaptive Graph Convolutional Network Based on Rotation Invariance.

IEEE journal of biomedical and health informatics
Studying the deep learning-based molecular representation has great significance on predicting molecular property, promoted the development of drug screening and new drug discovery, and improving human well-being for avoiding illnesses. It is essenti...

Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment.

Chemical research in toxicology
Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied acros...

Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks.

Molecules (Basel, Switzerland)
Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medicati...

An interpretable machine learning model for selectivity of small-molecules against homologous protein family.

Future medicinal chemistry
In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side ...