Results 61 to 70 of about 12,257 (212)

Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions

Toxics
Quantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches.
Benjamin Bajželj, Marjana Novič, Viktor Drgan   +2 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

CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes (Conference Abstract) [PDF]

, 2010
Sommer B, Dingersen T, Schneider S, Rubert S, Gamroth C. CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes (Conference Abstract). In: Journal of Cheminformatics. Journal of Cheminformatics.
S Rubert, Schneider, S., Rubert, S., Rubert S, C Gamroth, Dingersen T, Schneider S, Gamroth C, Sommer Björn, T Dingersen, Dingersen, T., Björn Sommer, S Schneider, Sommer, Björn, Gamroth, C.   +14 more
core   +2 more sources

Cheminformatics Microservice - Performance test results [PDF]

, 2023
Performance testing: Latency distribution and success rate at each request rate Ramping Load: To determine the maximum throughput the Cheminformatics Microservice (Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz and 16GB of RAM) can handle, we used the Vegeta
Nisha Sharma, Venkata Nainala, Kohulan Rajan   +2 more
core   +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

A cheminformatics review of auxins as herbicides [PDF]

Journal of Experimental Botany, 2017
Herbicides are an important asset in ensuring food security, especially when faced with an ever-increasing demand on food production to feed the global population. The current selection of herbicides is increasingly encountering resistance in agricultural weeds they once targeted effectively.
Mussa Quareshy, Justyna Prusinska, Jun Li, Richard Napier   +3 more
openaire   +3 more sources

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

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

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source