AIMC Topic: Drug Design

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Machine learning optimization of cross docking accuracy.

Computational biology and chemistry
Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening ...

Predicting drug target interactions using meta-path-based semantic network analysis.

BMC bioinformatics
BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and li...

The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM.

Computational and mathematical methods in medicine
Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To...

Antileishmanial activity of novel indolyl-coumarin hybrids: Design, synthesis, biological evaluation, molecular docking study and in silico ADME prediction.

Bioorganic & medicinal chemistry letters
In present work we have designed and synthesized total twelve novel 3-(3-(1H-indol-3-yl)-3-phenylpropanoyl)-4-hydroxy-2H-chromen-2-one derivatives 13(a-l) using Ho(3+) doped CoFe2O4 nanoparticles as catalyst and evaluated for their potential antileis...

Cenderitide: structural requirements for the creation of a novel dual particulate guanylyl cyclase receptor agonist with renal-enhancing in vivo and ex vivo actions.

European heart journal. Cardiovascular pharmacotherapy
AIMS: Cenderitide is a novel dual natriuretic peptide (NP) receptor chimeric peptide activator, which targets the particulate guanylyl cyclase B (pGC-B) receptor and pGC-A unlike native NPs. Cenderitide was engineered to retain the anti-fibrotic prop...

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.

BioMed research international
Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM ...

Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network.

Journal of biomedical informatics
Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may ...

Fully-automated synthesis of 16β-(18)F-fluoro-5α-dihydrotestosterone (FDHT) on the ELIXYS radiosynthesizer.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Noninvasive in vivo imaging of androgen receptor (AR) levels with positron emission tomography (PET) is becoming the primary tool in prostate cancer detection and staging. Of the potential (18)F-labeled PET tracers, (18)F-FDHT has clinically shown to...

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

BMC bioinformatics
BACKGROUND: Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when boun...

A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.

Journal of computer-aided molecular design
The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast pr...