AIMC Topic: Drug Discovery

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The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.

Drug design, development and therapy
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivot...

DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening.

Journal of chemical information and modeling
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address...

Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery.

Journal of chemical information and modeling
In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of new chemical entities is one of the most critical issues, which is a time-consuming process, immensely expensive, ...

A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.

Journal of chemical information and modeling
Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many ...

The current role and evolution of X-ray crystallography in drug discovery and development.

Expert opinion on drug discovery
INTRODUCTION: Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery...

Artificial intelligence for dementia drug discovery and trials optimization.

Alzheimer's & dementia : the journal of the Alzheimer's Association
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data ana...

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.

Journal of chemical information and modeling
Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, dete...

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Molecular diversity
Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differen...

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies.

Chemical research in toxicology
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches hav...

Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors.

Molecular diversity
Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into...