AIMC Topic: Drug Discovery

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In silico co-crystal design: Assessment of the latest advances.

Drug discovery today Sep 7, 2023
Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active ph...
Crystallization Pharmaceutical Preparations Drug Discovery Machine Learning
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Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

Chemical research in toxicology Sep 7, 2023
The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-ran...
Adverse Outcome Pathways Artificial Intelligence Drug Discovery Quantitative Structure-Activity Relationship Drug Industry
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DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information.

IEEE journal of biomedical and health informatics Sep 6, 2023
The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for o...
Humans Drug Discovery Deep Learning Peptides Neural Networks, Computer
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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 Sep 6, 2023
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...
Precision Medicine United States Food and Drug Administration Artificial Intelligence United States Humans Machine Learning Drug Discovery
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DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening.

Journal of chemical information and modeling Aug 31, 2023
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...
Deep Learning Drug-Related Side Effects and Adverse Reactions Molecular Structure Databases, Factual Drug Discovery Humans High-Throughput Screening Assays
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Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery.

Journal of chemical information and modeling Aug 21, 2023
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, ...
Drug Discovery Algorithms Pharmaceutical Preparations Artificial Intelligence Machine Learning
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A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.

Journal of chemical information and modeling Aug 18, 2023
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 ...
Protein Kinase Inhibitors Drug Discovery Machine Learning
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The current role and evolution of X-ray crystallography in drug discovery and development.

Expert opinion on drug discovery Aug 17, 2023
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...
Humans Proteins Artificial Intelligence Computational Biology Drug Discovery Crystallography, X-Ray
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Artificial intelligence for dementia drug discovery and trials optimization.

Alzheimer's & dementia : the journal of the Alzheimer's Association Aug 16, 2023
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...
Dementia Drug Discovery Artificial Intelligence Disease Progression Machine Learning Humans
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Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.

Journal of chemical information and modeling Aug 15, 2023
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...
Neural Networks, Computer Drug Discovery Biological Availability Learning Machine Learning
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