Results 121 to 130 of about 160,727 (166)

A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery. [PDF]

Int J Mol Sci
Lin H, Vogel H, Zhang H.
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Computational and immunoinformatics approaches for designing phytocompound-based drugs and a multi-epitope vaccine targeting FemA, a cell wall protein of Staphylococcus aureus. [PDF]

PLoS One
Shuvo MN, Al Arian T, Fuad F, Noman MH, Alam N, Himel MK, Shil A.   +6 more
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AI-Driven Design of Miniproteins as Potential Allosteric Modulators. [PDF]

Pharmaceuticals (Basel)
Liu X, Sun Y, Xia Y, Li H, Yan Z.
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A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing. [PDF]

Sci Rep
Gider V, Budak C.
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The Biggest Challenge for Prediction of Membrane Permeability of Cyclic Peptides: Scarce Data Availability. [PDF]

J Med Chem
Lamers C.
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GraphDTA for predicting drug–target binding affinity

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Large Language Model Agent for Modular Task Execution in Drug Discovery. [PDF]

J Chem Inf Model
Ock J, Meda RS, Badrinarayanan S, Aluru NS, Chandrasekhar A, Barati Farimani A.   +5 more
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Modality-DTA: Multimodality Fusion Strategy for Drug–Target Affinity Prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023
Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations.
Xixi Yang, Zhangming Niu, Yuansheng Liu, Bosheng Song, Weiqiang Lu, Li Zeng, Xiangxiang Zeng   +6 more
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Breaking the barriers of data scarcity in drug–target affinity prediction

Briefings in Bioinformatics, 2023
Abstract Accurate prediction of drug–target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive,
Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, Tie-Yan Liu, Rui Yan   +8 more
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