Results 41 to 50 of about 21,282 (192)

Unveiling the Potent Antiviral and Antioxidant Activities of an Aqueous Extract from Caesalpinia mimosoides Lamk: Cheminformatics and Molecular Docking Approaches

Foods, 2023
Our group previously demonstrated that Caesalpinia mimosoides Lamk exhibits many profound biological properties, including anticancer, antibacterial, and antioxidant activities. However, its antiviral activity has not yet been investigated.
Anuwatchakij Klamrak, Jaran Nabnueangsap, Jaraspim Narkpuk, Yutthakan Saengkun, Piyapon Janpan, Napapuch Nopkuesuk, Arunrat Chaveerach, Samaporn Teeravechyan, Shaikh Shahinur Rahman, Theerawat Dobutr, Poramet Sitthiwong, Pornsuda Maraming, Natsajee Nualkaew, Nisachon Jangpromma, Rina Patramanon, Sakda Daduang, Jureerut Daduang   +16 more
doaj   +1 more source

Leveraging 3D Molecular Spatial Visual Information and Multi‐Perspective Representations for Drug Discovery

Advanced Science, EarlyView.
A deep learning framework called MolVisGNN is proposed to fuse 3D molecular visual information of drugs with multi‐source features, which proves the importance of 3D molecular visual information of drugs and the advancedness of this model in the field of drug discovery, and provides a reference for how to more comprehensively express small molecule ...
Zimai Zhang, Xi Zhou, Yujie Qi, Xiaobo Zhu, Xun Deng, Feng Tan, Yuan Huang, Lun Hu, Zhuhong You, Pengwei Hu   +9 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches [PDF]

, 2011
With the advent of High Throughput Screening techniques, it is feasible to filter possible leads from a mammoth chemical space that can act against a particular target and inhibit its action.
Abhik Seal, Abhik Seal, Anurag Passi, OSDD Consortium, UC Abdul Jaleel   +4 more
core   +2 more sources

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

CDK-Taverna: an open workflow environment for cheminformatics

BMC Bioinformatics, 2010
Background Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug discovery. The recent release of large open access chemistry databases generates a demand for flexible tools to process them and discover new ...
Zielesny Achim, Willighagen Egon L, Kuhn Thomas, Steinbeck Christoph   +3 more
doaj   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Identification of Plk1 type II inhibitors by structure-based virtual screening [PDF]

, 2009
Protein kinases are targets for drug development. Dysregulation of kinase activity leads to various diseases, e.g. cancer, inflammation, diabetes. Human polo-like kinase 1 (Plk1), a serine/threonine kinase, is a cancer-relevant gene and a potential drug ...
Keppner, Sarah, Proschak, Ewgenij, Schneider, Gisbert (Prof. Dr.), Spänkuch-Schmitt, Birgit   +3 more
core   +1 more source

The thermodynamic landscape of carbon redox biochemistry [PDF]

, 2018
Redox biochemistry plays a key role in the transduction of chemical energy in all living systems. Observed redox reactions in metabolic networks represent only a minuscule fraction of the space of all possible redox reactions.
Aspuru-Guzik, Alán, Goldford, Joshua, Jinich, Adrian, Noor, Elad, Ren, Haniu, Sanchez-Lengeling, Benjamin, Sanders, Jacob, Segre, Daniel   +7 more
core   +1 more source

Kernel learning for ligand-based virtual screening: discovery of a new PPARgamma agonist [PDF]

, 2010
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual ...
Hansen, Katja, Müller, Klaus-Robert, Proschak, Ewgenij, Rau, Oliver, Rupp, Matthias, Schneider, Gisbert (Prof. Dr.), Schroeter, Timon, Schubert-Zsilavecz, Manfred (Prof. Dr.), Steri, Ramona, Zettl, Heiko   +9 more
core