AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Thermodynamics

Showing 161 to 170 of 263 articles

Clear Filters

Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter.

BMC bioinformatics Dec 24, 2019
BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, a...
Nucleic Acid Conformation Base Pairing Thermodynamics Neural Networks, Computer RNA
View on PubMed DOI

Latest trends in structure based drug design with protein targets.

Advances in protein chemistry and structural biology Dec 18, 2019
Structure based drug designing is an important endeavor in the field of structural bioinformatics. Previously the entire process was dependent on the wet-lab experiments to build libraries of ligand molecules. And the molecules used to be tested to d...
Ligands Binding Sites Thermodynamics Structure-Activity Relationship Molecular Dynamics Simulation Drugs, Investigational Humans Anti-Bacterial Agents Antineoplastic Agents Computational Biology Machine Learning Molecular Docking Simulation Anti-HIV Agents Neuroprotective Agents Drug Design Protein Binding Proteins
View on PubMed DOI

Finding Reactive Configurations: A Machine Learning Approach for Estimating Energy Barriers Applied to Sirtuin 5.

Journal of chemical theory and computation Nov 25, 2019
Sirtuin 5 is a class III histone deacetylase that, unlike its classification, mainly catalyzes desuccinylation and demanoylation reactions. It is an interesting drug target that we use here to test new ideas for calculating reaction pathways of large...
Molecular Dynamics Simulation Quantum Theory Protein Conformation Humans Machine Learning Thermodynamics Sirtuins
View on PubMed DOI

Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors.

Journal of chemical information and modeling Nov 21, 2019
Convolutional neural network (CNN) is employed to construct generative and prediction models for the design and analysis of non-fullerene acceptors (NFAs) in organic solar cells. It is demonstrated that the dilated causal CNN can be trained as a good...
Neural Networks, Computer Drug Design Organic Chemicals Solar Energy Thermodynamics Electric Power Supplies
View on PubMed DOI

Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors.

Journal of computer-aided molecular design Nov 19, 2019
Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from ...
Small Molecule Libraries Solvents Thermodynamics Humans Machine Learning Drug Discovery ATP Binding Cassette Transporter, Subfamily B, Member 1
View on PubMed DOI

MathDL: mathematical deep learning for D3R Grand Challenge 4.

Journal of computer-aided molecular design Nov 16, 2019
We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4). This challenge involves pose prediction, affinity ranking, and free energy estimation for beta secretase 1 (BACE) as well as affinity ranki...
Drug Design Deep Learning Protein Binding Ligands Amyloid Precursor Protein Secretases Aspartic Acid Endopeptidases Humans Molecular Docking Simulation Binding Sites Small Molecule Libraries Thermodynamics Cathepsins
View on PubMed DOI

Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Journal of computer-aided molecular design Nov 15, 2019
Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for...
Binding Sites Thermodynamics Cathepsins Crystallography, X-Ray Machine Learning Protein Conformation Molecular Docking Simulation Computer-Aided Design Humans Protein Binding Databases, Protein Ligands
View on PubMed DOI

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling Nov 5, 2019
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...
Density Functional Theory Models, Molecular Gold Neural Networks, Computer Computer Simulation Crystallization Alloys Thermodynamics Models, Chemical Titanium Surface Properties
View on PubMed DOI

Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

Biomolecules Oct 14, 2019
The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, se...
Supervised Machine Learning Proteins Protein Conformation Databases, Protein Thermodynamics
View on PubMed DOI

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields.

Journal of chemical information and modeling Sep 27, 2019
We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. ...
Chemical Engineering Bayes Theorem Machine Learning Molecular Dynamics Simulation Algorithms Thermodynamics
View on PubMed DOI
Popular Topics
Recent Journals