Results 21 to 30 of about 57,588 (165)

Industry-scale application and evaluation of deep learning for drug target prediction [PDF]

, 2019
Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling.
Ashby, Thomas J., Böhm, Stanislav, Ceulemans, Hugo, Chen, Hongming, Chupakhin, Vladimir, Cima, Vojtěch, Engkvist, Ola, Golib-Dzib, Jose-Felipe, Greene, Nigel, Hochreiter, Sepp, Jeliazkova, Nina, Klambauer, Günter, Martinovič, Jan, Mayr, Andreas, Sturm, Noe, Van, Thanh Le, Vander Aa, Tom, Vandriessche, Yves, Wegner, Joerg   +18 more
core   +2 more sources

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

, 2020
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and ...
Barzilay, Regina, Coley, Connor W., Hirschfeld, Lior, Swanson, Kyle, Yang, Kevin   +4 more
core   +1 more source

Global Antifungal Profile Optimization of Chlorophenyl Derivatives against Botrytis cinerea and Colletotrichum gloeosporioides [PDF]

, 2009
Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the phytopathogenic fungi Botrytis cinerea and Colletotrichum ...
Aleu J., Aleu J., Aleu J., Barbosa D., Barbosa de Oliveira D., Burkert U., Bustillo A. J., Bustillo A. J., Bustillo A. J., Clark T., Coleman T. F., Coleman T. F., Coley-Smith J. R., Colmenares A. J., Cruciani G., Cruz-Monteagudo M., Cruz-Monteagudo M., De Boor C., Duke S. O., Echeverri F., Efron B., Eriksson L., Gerald C. F., Gramatica P., Hasegawa K., Hou T. J., Leardi R., Patil I. S., Reino J. L., Saiz-Urra L., Van Waterbeemd H., Yasri A.   +31 more
core   +2 more sources

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression [PDF]

, 2018
This is the pre-peer reviewed version of the following article: Parivash Ashrafi, Yi Sun, Neil Davey, Roderick G. Adams, Simon C. Wilkinson, and Gary Patrick Moss, ‘Model fitting for small skin permeability data sets: hyperparameter optimisation in ...
Adams, Roderick, Ashrafi, Parivash, Davey, Neil, Moss, Gary Patrick, Sun, Yi, Wilkinson, Simon C   +5 more
core   +3 more sources

Polar Lattice‐Distorted Motifs Enable Synergy of Local Polarization/Dipole Fields for Concurrent Glyphosate Wastewater Remediation and CO Evolution

Advanced Science, EarlyView.
Photocatalytic treatment of glyphosate herbicide in agricultural wastewater is achieved through the cooperative effect of the local polarization field and dipole field mediated by lattice‐distorted carbon nitride. Glyphosate is completely degraded via selective C─P bond cleavage with a CO evolution rate of 1166 µmol g−1 h−1.
Daoping Chen, Huan Liu, Tengyu Liu, Jie Li, Qizhi Luo, Yan Zhang, Shengkun Li, Hu Li   +7 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

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

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