AIMC Topic: Models, Molecular

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Machine Learning for Molecular Modelling in Drug Design.

Biomolecules
Machine learning (ML) has become a crucial component of early drug discovery. This researcharea has been fueled by two main factors [...].

Pharmacophore features for machine learning in pharmaceutical virtual screening.

Molecular diversity
Methods of three-dimensional molecular alignment generally treat all pharmacophore features equally when superimposing. However, some pharmacophore features can be more important in a specific system. In this work, we derived the overlap volume of ph...

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds.

Molecules (Basel, Switzerland)
P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties...

Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines.

Journal of chemical information and modeling
A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and...

MCP: A multi-component learning machine to predict protein secondary structure.

Computers in biology and medicine
The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through the translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightl...

PiPred - a deep-learning method for prediction of π-helices in protein sequences.

Scientific reports
Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- an...

Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling.

Molecules (Basel, Switzerland)
The performance of quantitative structure-activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main catego...

In silico minimalist approach to study 2D HP protein folding into an inhomogeneous space mimicking osmolyte effect: First trial in the search of foldameric backbones.

Bio Systems
We have employed our bioinformatics workbench, named Evolution, a Multi-Agent System based architecture with lattice-bead-models, evolutionary-algorithms, and correlated-networks as inhomogeneous spaces, with different correlation lengths, mimicking ...

DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines.

Journal of chemical information and modeling
Accurate identification of protein-DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the prediction of protein-DNA binding sites. However, the data ...