AIMC Topic: Drug Discovery

Clear Filters Showing 601 to 610 of 1689 articles

Artificial intelligence and machine learning for clinical pharmacology.

British journal of clinical pharmacology
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in...

STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks.

Computers in biology and medicine
With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training proc...

Deep Generative Models in Drug Molecule Generation.

Journal of chemical information and modeling
The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence ...

Artificial intelligence: Machine learning approach for screening large database and drug discovery.

Antiviral research
Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer...

Computational methods in glaucoma research: Current status and future outlook.

Molecular aspects of medicine
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identif...

Automation to Enable High-Throughput Chemical Proteomics.

Journal of proteome research
Chemical proteomics utilizes small-molecule probes to covalently engage with their interacting proteins. Since chemical probes are tagged to the active or binding sites of functional proteins, chemical proteomics can be used to profile protein target...

Extracting medicinal chemistry intuition via preference machine learning.

Nature communications
The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects...

Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening.

Journal of chemical information and modeling
Structure-based virtual screening has been a crucial tool in drug discovery for decades. However, as the chemical space expands, the existing structure-based virtual screening techniques based on molecular docking and scoring struggle to handle billi...

Rationalizing general limitations in assessing and comparing methods for compound potency prediction.

Scientific reports
Compound potency predictions play a major role in computational drug discovery. Predictive methods are typically evaluated and compared in benchmark calculations that are widely applied. Previous studies have revealed intrinsic limitations of potency...

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.

Journal of biomolecular structure & dynamics
The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can...