AIMC Topic: Drug Discovery

Clear Filters Showing 721 to 730 of 1689 articles

Everything is connected: Graph neural networks.

Current opinion in structural biology
In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent...

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Molecular pharmaceutics
Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to...

New avenues in artificial-intelligence-assisted drug discovery.

Drug discovery today
Over the past decade, the amount of biomedical data available has grown at unprecedented rates. Increased automation technology and larger data volumes have encouraged the use of machine learning (ML) or artificial intelligence (AI) techniques for mi...

Chemistry42: An AI-Driven Platform for Molecular Design and Optimization.

Journal of chemical information and modeling
Chemistry42 is a software platform for small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structu...

Industrializing AI/ML during the end-to-end drug discovery process.

Current opinion in structural biology
Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several ar...

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

Journal of translational medicine
BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions prese...

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

International journal of molecular sciences
The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation...

Transformer-based deep learning method for optimizing ADMET properties of lead compounds.

Physical chemistry chemical physics : PCCP
A successful drug needs to exhibit both effective pharmacodynamics (PD) and safe pharmacokinetics (PK). However, the coordinated optimization of PD and PK properties in molecule generation tasks remains a great challenge for most existing methods, es...

Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction.

International journal of molecular sciences
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures...