AIMC Topic: Drug Discovery

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Organic Compound Synthetic Accessibility Prediction Based on the Graph Attention Mechanism.

Journal of chemical information and modeling Jun 8, 2022
Accurate estimation of the synthetic accessibility of small molecules is needed in many phases of drug discovery. Several expert-crafted scoring methods and descriptor-based quantitative structure-activity relationship (QSAR) models have been develop...
Neural Networks, Computer Machine Learning Drug Discovery Bayes Theorem Quantitative Structure-Activity Relationship
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QMugs, quantum mechanical properties of drug-like molecules.

Scientific data Jun 7, 2022
Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecu...
Machine Learning Drug Discovery Thermodynamics
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Retro Drug Design: From Target Properties to Molecular Structures.

Journal of chemical information and modeling Jun 2, 2022
To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods...
Humans Artificial Intelligence Machine Learning Drug Design Drug Discovery Molecular Structure
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On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks.

Journal of medicinal chemistry May 24, 2022
Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in their free...
Neural Networks, Computer Drug Discovery Protein Binding Proteins Ligands
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Artificial intelligence in virtual screening: Models versus experiments.

Drug discovery today May 18, 2022
A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensiv...
Artificial Intelligence Machine Learning Drug Discovery Small Molecule Libraries
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Data considerations for predictive modeling applied to the discovery of bioactive natural products.

Drug discovery today May 14, 2022
Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the larg...
Artificial Intelligence Drug Discovery Drug Development Biological Products
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Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.

Current opinion in chemical biology May 13, 2022
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein ...
Machine Learning Drug Discovery Deep Learning Proteins Ligands
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Bench to bedside: The ambitious goal of transducing medicinal chemistry from the lab to the clinic.

Bioorganic & medicinal chemistry letters May 12, 2022
This paper deals with a critical examination on the possibility of quantitatively predicting the in vivo activity of new chemical entities (NCEs) by making use of in silico and in vitro data including three-dimensional structure of drug-target comple...
Artificial Intelligence Algorithms Drug Discovery Goals Chemistry, Pharmaceutical
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Artificial intelligence in cancer target identification and drug discovery.

Signal transduction and targeted therapy May 10, 2022
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying...
Humans Artificial Intelligence Machine Learning Algorithms Neoplasms Drug Discovery
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Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology.

Molecules (Basel, Switzerland) May 8, 2022
Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Mac...
Humans Machine Learning Algorithms Drug Discovery Proteomics Proteins
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