AIMC Topic: Drug Discovery

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MD-GNN: A mechanism-data-driven graph neural network for molecular properties prediction and new material discovery.

Journal of molecular graphics & modelling May 9, 2023
Molecular properties prediction and new material discovery are significant for the pharmaceutical industry, food, chemistry, and other fields. The popular methods are theoretical mechanism calculation and machine learning. There is a deviation betwee...
Drug Discovery Reproducibility of Results Machine Learning Neural Networks, Computer
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Protein model quality assessment using rotation-equivariant transformations on point clouds.

Proteins May 9, 2023
Machine learning research concerning protein structure has seen a surge in popularity over the last years with promising advances for basic science and drug discovery. Working with macromolecular structure in a machine learning context requires an ad...
Neural Networks, Computer Proteins Rotation Drug Discovery Machine Learning
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AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease.

International journal of molecular sciences Apr 29, 2023
Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs use...
Coronavirus 3C Proteases Artificial Intelligence Drug Discovery Neural Networks, Computer COVID-19 Drug Treatment Small Molecule Libraries Models, Molecular
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Explainability and white box in drug discovery.

Chemical biology & drug design Apr 27, 2023
Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision pr...
Drug Discovery Algorithms Drug Delivery Systems Machine Learning Artificial Intelligence
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Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives.

Journal of chemical information and modeling Apr 26, 2023
Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is esti...
Machine Learning Drug Discovery Algorithms Humans Artificial Intelligence Biological Assay
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Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration.

BMC bioinformatics Apr 26, 2023
The flourishment of machine learning and deep learning methods has boosted the development of cheminformatics, especially regarding the application of drug discovery and new material exploration. Lower time and space expenses make it possible for sci...
Drug Discovery Deep Learning Drug Design Machine Learning Neural Networks, Computer
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DeepCancerMap: A versatile deep learning platform for target- and cell-based anticancer drug discovery.

European journal of medicinal chemistry Apr 23, 2023
Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensiv...
Deep Learning Antineoplastic Agents Cell Line, Tumor Machine Learning Drug Discovery
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DrugormerDTI: Drug Graphormer for drug-target interaction prediction.

Computers in biology and medicine Apr 23, 2023
Drug-target interactions (DTI) prediction is a crucial task in drug discovery. Existing computational methods accelerate the drug discovery in this respect. However, most of them suffer from low feature representation ability, significantly affecting...
Drug Development Drug Interactions Proteins Molecular Docking Simulation Neural Networks, Computer Drug Discovery
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Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit.

Journal of chemical information and modeling Apr 19, 2023
Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein...
Proteins Cheminformatics Drug Discovery Neural Networks, Computer
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Harmonizing across datasets to improve the transferability of drug combination prediction.

Communications biology Apr 11, 2023
Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the respon...
High-Throughput Screening Assays Drug Discovery Drug Combinations Machine Learning
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