AIMC Topic: Structure-Activity Relationship

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Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization.

Journal of computer-aided molecular design Sep 18, 2017
We present a novel optimization approach to train a free-shape distance-dependent protein-ligand scoring function called Convex-PL. We do not impose any functional form of the scoring function. Instead, we decompose it into a polynomial basis and ded...
Binding Sites Knowledge Bases Ligands Protein Conformation Humans Proteins Molecular Docking Simulation Machine Learning Algorithms Databases, Protein Drug Design Structure-Activity Relationship Protein Binding Molecular Structure
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Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models.

European journal of medicinal chemistry Sep 1, 2017
There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is little drug information with which to start drug candidate searches. Typical high-throughput screening...
Humans Algorithms Principal Component Analysis Support Vector Machine Models, Molecular Molecular Structure Factor XIIa Dose-Response Relationship, Drug Blood Proteins Structure-Activity Relationship
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Bioactivity and structure-activity relationship of cinnamic acid derivatives and its heteroaromatic ring analogues as potential high-efficient acaricides against Psoroptes cuniculi.

Bioorganic & medicinal chemistry letters Aug 24, 2017
A series of cinnamic acid derivatives and its heteroaromatic ring analogues were synthesized and evaluated for acaricidal activity in vitro against Psoroptes cuniculi, a mange mite. Among them, eight compounds showed the higher activity with median l...
Molecular Structure Animals Psoroptidae Cinnamates Dose-Response Relationship, Drug Acaricides Structure-Activity Relationship
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Bias-Free Chemically Diverse Test Sets from Machine Learning.

ACS combinatorial science Jul 27, 2017
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal an...
Databases, Chemical Thermodynamics Principal Component Analysis Models, Chemical Quantum Theory Cluster Analysis Structure-Activity Relationship Machine Learning
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Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.

Journal of chemical information and modeling Jul 10, 2017
Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with differ...
Humans Machine Learning Transcription Factors Cell Line Structure-Activity Relationship Drug Evaluation, Preclinical Molecular Docking Simulation Drug Discovery Nuclear Proteins Molecular Targeted Therapy Protein Conformation Cell Cycle Proteins
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Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

Immunology Jun 19, 2017
MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or les...
Structure-Activity Relationship Mutation Epitopes Ligands Computational Biology Binding Sites Databases, Protein Protein Interaction Domains and Motifs Humans Antibodies, Monoclonal Histocompatibility Antigens Class II Peptides Protein Binding Neural Networks, Computer Machine Learning
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Novel Imidazo[4,5-c][1,2,6]thiadiazine 2,2-dioxides as antiproliferative trypanosoma cruzi drugs: Computational screening from neural network, synthesis and in vivo biological properties.

European journal of medicinal chemistry May 1, 2017
A new family of imidazo[4,5-c][1,2,6]thiadiazine 2,2-dioxide with antiproliferative Trypanosoma cruzi properties was identified from a neural network model published by our group. The synthesis and evaluation of this new class of trypanocidal agents ...
Macrophages Animals Neural Networks, Computer Trypanosoma cruzi Thiadiazines Dose-Response Relationship, Drug Mice Structure-Activity Relationship Imidazoles Cell Proliferation Molecular Structure Fibroblasts
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Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Artificial intelligence in medicine Mar 4, 2017
Computational methods are employed in bioinformatics to predict protein-protein interactions (PPIs). PPIs and protein-protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that o...
Structure-Activity Relationship Natural Language Processing Amino Acid Sequence Protein Structure, Secondary Computational Biology Proteins Databases, Protein Humans Reproducibility of Results Protein Interaction Maps
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Study of Structure-active Relationship for Inhibitors of HIV-1 Integrase LEDGF/p75 Interaction by Machine Learning Methods.

Molecular informatics Feb 28, 2017
HIV-1 integrase (IN) is a promising target for anti-AIDS therapy, and LEDGF/p75 is proved to enhance the HIV-1 integrase strand transfer activity in vitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replicati...
Intercellular Signaling Peptides and Proteins HIV Integrase Inhibitors Molecular Conformation Structure-Activity Relationship Models, Molecular Molecular Structure Humans HIV Integrase ROC Curve Computer Simulation Machine Learning Protein Binding Reproducibility of Results
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Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.

Molecular diversity Feb 9, 2017
The Average Information Content Maximization algorithm (AIC-MAX) based on mutual information maximization was recently introduced to select the most discriminatory features. Here, this methodology was applied to select the most significant bits from ...
Structure-Activity Relationship Ligands Informatics Receptors, Serotonin Machine Learning Drug Discovery
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