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Models, Molecular

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GuacaMol: Benchmarking Models for de Novo Molecular Design.

Journal of chemical information and modeling Mar 19, 2019
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural net...
Drug Design Deep Learning Benchmarking Isomerism Quantitative Structure-Activity Relationship Monte Carlo Method Models, Molecular Molecular Structure Pharmaceutical Preparations
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Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling Mar 5, 2019
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...
Computers, Molecular Quantitative Structure-Activity Relationship Receptors, FSH Binding Sites Protein Binding Models, Molecular Computational Biology Ligands Cell Line Hydrophobic and Hydrophilic Interactions Humans Machine Learning Drug Design
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Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling Feb 28, 2019
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...
Molecular Structure Pharmaceutical Preparations Hydrophobic and Hydrophilic Interactions Machine Learning Drug Design Models, Molecular Molecular Conformation Quantitative Structure-Activity Relationship Hydrogen Bonding
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Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

Molecules (Basel, Switzerland) Feb 19, 2019
The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, includ...
Spectrum Analysis Molecular Conformation beta-Lactams Structure-Activity Relationship Models, Molecular Cyclodextrins Machine Learning
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MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics.

BioMed research international Feb 17, 2019
The selection of the most relevant molecular descriptors to describe a target variable in the context of QSAR (Quantitative Structure-Activity Relationship) modelling is a challenging combinatorial optimization problem. In this paper, a novel softwar...
Machine Learning Software Models, Molecular Quantitative Structure-Activity Relationship
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Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues.

Computational biology and chemistry Feb 16, 2019
BACKGROUND/AIM: Main objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline m...
Structure-Activity Relationship Models, Molecular Molecular Structure Gallic Acid PC-3 Cells Support Vector Machine Dose-Response Relationship, Drug Drug Screening Assays, Antitumor Time Factors Cell Line, Tumor Cell Proliferation Humans Artificial Intelligence Machine Learning Algorithms Antineoplastic Agents
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Identification of coenzyme-binding proteins with machine learning algorithms.

Computational biology and chemistry Jan 28, 2019
The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on th...
Humans Software Molecular Structure Machine Learning Proteins Esters Models, Molecular Acyl Coenzyme A
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Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Journal of chemical information and modeling Jan 25, 2019
Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds...
Proteins Models, Molecular Pharmaceutical Preparations Databases, Pharmaceutical Machine Learning High-Throughput Screening Assays ROC Curve Binding Sites Protein Binding Small Molecule Libraries
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Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets.

Journal of chemical information and modeling Jan 24, 2019
Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excr...
Humans Neural Networks, Computer Drug Design Models, Statistical Databases, Pharmaceutical Models, Biological Permeability Caco-2 Cells Quantitative Structure-Activity Relationship Models, Molecular Pharmaceutical Preparations
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DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

PloS one Jan 8, 2019
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving t...
Protein Structure, Tertiary Sequence Analysis, Protein Databases, Protein Models, Molecular Amino Acid Sequence Computational Biology Support Vector Machine Proteins Algorithms Deep Learning
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