Results 31 to 40 of about 57,588 (165)
Fast training of self organizing maps for the visual exploration of molecular compounds [PDF]
, 2007 Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data.
Di Fatta, Giuseppe +4 more
core +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
Advanced Intelligent Discovery, EarlyView.This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Correlations between experimental and theoretical adiabatic ionization energies for organic compounds and rate constants for atmospheric reactions with hydroxyl radicals [PDF]
, 2010 Adiabatic ionization energy (AIE) calculations were performed at the AM1, PM3, PM6, PDDG, HF/QZVP, and B3LYP/QZVP levels of theory on 722 atmospherically relevant organic compounds with available experimental rate constants for atmospheric reactions with
Kaya Forest, Sierra Rayne
core +2 more sources
Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics
Advanced Intelligent Discovery, EarlyView.A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha +2 more
wiley +1 more source
Journal of Applied Polymer Science, EarlyView.This study develops an interpretable machine‐learning framework to predict multiple properties of polymer composites based on composition and processing variables. By combining ensemble models with composition‐based feature generation and SHAP‐based interpretation, the approach reveals composition‐property relationships and supports efficient multi ...
Dong Ryeol Shin, Sung Kwang Lee
wiley +1 more source
Higher Efficiency In Prediction Of TIBO Activity By Evolutionary Neural Network [PDF]
, 2011 The treatment of acquired immunodeficiency syndrome (AIDS) is a challenging medical problem. TIBO is a nonnucleoside reverse transcriptase inhibitor, which binds non-competitively to the hydrophobic pocket on the p66 subunit of RT enzyme.
Abhik Seal
core +1 more source
, 2013 Interrelated Two-way Clustering (ITC) is an unsupervised clustering method developed to divide samples into two groups in gene expression data obtained through microarrays, selecting important genes simultaneously in the process.
Basak, Subhash C. +2 more
core +1 more source
GTI-space : the space of generalized topological indices [PDF]
, 2008 A new extension of the generalized topological indices (GTI) approach is carried out torepresent 'simple' and 'composite' topological indices (TIs) in an unified way.
A.R Matamala +34 more
core +1 more source
Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose. [PDF]
, 2018 We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket ...
Cleves, Ann E, Jain, Ajay N
core
Gaussian Processes for Predictive QSAR Modeling of Chromatographic Processes
Biotechnology and Bioengineering, EarlyView.ABSTRACT Chromatography is a key unit operation in the biopharmaceutical manufacturing process used for protein purification and polishing. Design and optimization of these processes are resource‐intensive resulting from the complex combinatorial design space.
Harini Narayanan +7 more
wiley +1 more source