Results 71 to 80 of about 24,906 (269)

Multi‐View Biomedical Foundation Models for Molecule‐Target and Property Prediction

Advanced Science, EarlyView.
Molecular foundation models can provide accurate predictions for a large set of downstream tasks. We develop MMELON, an approach that integrates pre‐trained graph, image, and text foundation models and validate our multi‐view model on over 120 tasks, including GPCR binding.
Parthasarathy Suryanarayanan, Yunguang Qiu, Shreyans Sethi, Diwakar Mahajan, Hongyang Li, Yuxin Yang, Elif Eyigoz, Aldo Guzmán‐Sáenz, Daniel E. Platt, Timothy H. Rumbell, Kenney Ng, Sanjoy Dey, Myson Burch, Bum Chul Kwon, Pablo Meyer, Feixiong Cheng, Jianying Hu, Joseph A. Morrone   +17 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

Beyond Cocrystals: Hierarchical Functional Assemblies via Noncovalent Synthesis

Advanced Science, EarlyView.
Non‐covalent interactions (NCIs) drive the formation of organic cocrystals with diverse structures and tunable optoelectronic properties. This review explores the essential factors governing these properties, highlighting how the sequential nucleation of cocrystals leads to the self‐assembly of organic hierarchical structures (OHSs).
Ya‐Nan Zhu, Jin Feng, Ying‐Xin Ma, Hong‐Tao Lin, Li‐Wei Xie, Qi‐Liang Peng, Xue‐Dong Wang   +6 more
wiley   +1 more source

A Brief Review of Machine Learning-Based Bioactive Compound Research

Applied Sciences, 2022
Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired ...
Jihye Park, Bo Ram Beck, Hoo Hyun Kim, Sangbum Lee, Keunsoo Kang   +4 more
doaj   +1 more source

Cheminformatics-based identification of phosphorylated RET tyrosine kinase inhibitors for human cancer

Frontiers in Chemistry
Background Rearranged during transfection (RET), an oncogenic protein, is associated with various cancers, including non-small-cell lung cancer (NSCLC), papillary thyroid cancer (PTC), pancreatic cancer, medullary thyroid cancer (MTC), breast cancer, and
Md. Enamul Kabir Talukder, Md. Aktaruzzaman, N. H. Siddiquee, Sabrina Islam, Tanveer A. Wani, H. Alkahtani, Seema Zargar, Md. Obayed Raihan, Md. Mashiar Rahman, Sushil Pokhrel, Foysal Ahammad   +10 more
semanticscholar   +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

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

Cheminformatics approach to identify andrographolide derivatives as dual inhibitors of methyltransferases (nsp14 and nsp16) of SARS-CoV-2

Scientific Reports
The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection.
Jobin Thomas, Anupam Ghosh, Shivendu Ranjan, J. Satija   +3 more
semanticscholar   +1 more source

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

Cheminformatics in Natural Product‐based Drug Discovery

Molecular Informatics, 2020
This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product‐based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products ...
Ya Chen, J. Kirchmair
semanticscholar   +1 more source