AIMC Topic: Drug Discovery

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Benford's Law and distributions for better drug design.

Expert opinion on drug discovery Feb 1, 2024
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 ...
Humans Artificial Intelligence Drug Design Drug Discovery Machine Learning Research Design
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Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.

Journal of chemical information and modeling Jan 31, 2024
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...
Gene Expression Profiling Transcriptome Deep Learning Humans Drug Discovery Malaria
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Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Journal of chemical information and modeling Jan 31, 2024
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 ...
Drug Discovery Artificial Intelligence Humans Membrane Transport Proteins Computational Chemistry Drug Design
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SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery.

Journal of chemical information and modeling Jan 30, 2024
SerotoninAI is an innovative web application for scientific purposes focused on the serotonergic system. By leveraging SerotoninAI, researchers can assess the affinity (pKi value) of a molecule to all main serotonin receptors and serotonin transporte...
Web Browser Drug Discovery Internet Software Humans Artificial Intelligence
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ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

Journal of chemical information and modeling Jan 30, 2024
The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods ...
Artificial Intelligence Software Drug Discovery Reproducibility of Results Problem-Based Learning
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MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction.

Methods (San Diego, Calif.) Jan 28, 2024
The accurate identification of drug-protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR-drug pairin...
Deep Learning Neural Networks, Computer Drug Interactions Drug Development Drug Discovery
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Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.

Transboundary and emerging diseases Jan 17, 2024
, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiting skin lesions to potentially fatal visceral complications. As such, the parasite has been the subj...
Leishmaniasis Vaccines Vaccine Development Antiprotozoal Agents Leishmaniasis Leishmania Drug Discovery Deep Learning Animals Humans
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Predictive Minisci late stage functionalization with transfer learning.

Nature communications Jan 15, 2024
Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates t...
Neural Networks, Computer Machine Learning Drug Discovery Prospective Studies Retrospective Studies
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MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design.

Scientific reports Jan 12, 2024
Generative Artificial Intelligence can be an important asset in the drug discovery process to meet the demand for novel medicines. This work outlines the optimization and fine-tuning steps of MedGAN, a deep learning model based on Wasserstein Generat...
Artificial Intelligence Quinolines Drug Discovery Neurons Drug Design
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AI for targeted polypharmacology: The next frontier in drug discovery.

Current opinion in structural biology Jan 11, 2024
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise ...
Artificial Intelligence Drug Discovery Machine Learning Polypharmacology Drug Design
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