AI Medical Compendium Topic:
Drug Design

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The role of different sampling methods in improving biological activity prediction using deep belief network.

Journal of computational chemistry
Thousands of molecules and descriptors are available for a medicinal chemist thanks to the technological advancements in different branches of chemistry. This fact as well as the correlation between them has raised new problems in quantitative struct...

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

Molecular informatics
Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems hav...

ROCS-derived features for virtual screening.

Journal of computer-aided molecular design
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. I...

Molecular graph convolutions: moving beyond fingerprints.

Journal of computer-aided molecular design
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structur...

A renaissance of neural networks in drug discovery.

Expert opinion on drug discovery
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the mo...

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

Expert opinion on drug discovery
INTRODUCTION: Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably...

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...