AIMC Topic: Drug Discovery

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SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery.

Journal of chemical information and modeling
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...

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

Journal of chemical information and modeling
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 ...

MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction.

Methods (San Diego, Calif.)
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...

Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.

Transboundary and emerging diseases
, 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...

Predictive Minisci late stage functionalization with transfer learning.

Nature communications
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...

MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design.

Scientific reports
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...

AI for targeted polypharmacology: The next frontier in drug discovery.

Current opinion in structural biology
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 ...

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to b...

Generative artificial intelligence empowers digital twins in drug discovery and clinical trials.

Expert opinion on drug discovery
INTRODUCTION: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulation...