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Improving Predictions with a Multitask Convolutional Siamese Network.

Journal of chemical information and modeling
The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Rel...

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events.

Journal of chemical theory and computation
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to ...

Contingency response decision of network public opinion emergencies based on intuitionistic fuzzy entropy and preference information of decision makers.

Scientific reports
A multi-attribute group decision-making (MAGDM) method based on intuitionistic fuzzy preference information is proposed for the multi-attribute intuitionistic fuzzy group decision-making problem where the decision-makers weight and attribute weight a...

Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining.

The journal of physical chemistry. A
Molecular dynamics (MD) simulations are widely used to obtain the microscopic properties of atomistic systems when the interatomic potential or the coarse-grained potential is known. In many practical situations, however, it is necessary to predict t...

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling.

Journal of chemical theory and computation
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations...

A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.

International journal of molecular sciences
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Ab...

Clean energy selection for sustainable development by using entropy-based decision model with hesitant fuzzy information.

Environmental science and pollution research international
Smart cities development is an ambitious project launched in India in 2015 with around 14 billion USD. Smart city mission program primarily aimed at reducing the carbon footprint and encouraging green and sustainable practices. Under this context, cl...

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy.

Journal of chemical information and modeling
We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed gr...

Semi-supervised learning for medical image classification using imbalanced training data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Medical image classification is often challenging for two reasons: a lack of labelled examples due to expensive and time-consuming annotation protocols, and imbalanced class labels due to the relative scarcity of disease-pos...

Granularity and Entropy of Intuitionistic Fuzzy Information and Their Applications.

IEEE transactions on cybernetics
A granular structure of intuitionistic fuzzy (IF) information presents simultaneously the similarity and diversity of samples. However, this structural representation has rarely displayed its technical capability in data mining and information proces...