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Open AccessJournal ArticleDOI

Computational methods in drug discovery.

TLDR
An overview of computational methods used in different facets of drug discovery and highlight some of the recent successes is presented, both structure-based and ligand-based drug discovery methods are discussed.
Abstract
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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Book ChapterDOI

Influence of Tween 80 Surfactant on the Binding of Roxatidine Acetate and Roxatidine Acetate–loaded Chitosan Nanoparticles to Lysozyme

TL;DR: In this paper , a nanocomposite material based on chitosan (Cs) and Roxatidine acetate (RxAc) in the presence of Tween 80 (Tw80) surfactant was developed.
Journal ArticleDOI

In silico studies on recreational drugs: 3D quantitative structure activity relationship prediction of classified and de novo designer benzodiazepines

TL;DR: QSAR could be of use as a preliminary risk assessment model for (newly) identified DBZDs, as well as scaffold hopping for the creation of computational libraries that could be used by regulatory bodies as support tools for scheduling procedures.
Journal ArticleDOI

Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction

TL;DR: In this paper , a few-shot learning strategy across graph neural networks and convolutional neural networks is proposed to leverage the rich information of graph embeddings, and a two-module meta-learning framework is used to learn from task-transferable knowledge and predict molecular properties on fewshot data.
Posted Content

ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning.

TL;DR: In this article, a ResNet neural network with added attention mechanism was used to predict protein-ligand affinity through the ResAtom-score model, which achieved Pearson's correlation coefficient R = 0.833 on the CASF-2016 benchmark test set.
Journal ArticleDOI

Calculating the stability of molecular interface between the ligand-complex and solvent molecule: A study of Averrhoa bilimbi bioactive compounds as anti-diabetic agent

TL;DR: In this paper , the stability of the molecular interface between the solute and solvent of the molecule through molecular dynamics simulation was evaluated and calculated. And the results of molecular surface area after dynamics simulation of these two compounds showed a unique and stable pattern compared to miglitol, a controlled drug for diabetes.
References
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宁北芳, +1 more
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
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CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

TL;DR: The CHARMM (Chemistry at Harvard Macromolecular Mechanics) as discussed by the authors is a computer program that uses empirical energy functions to model macromolescular systems, and it can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations.
Journal ArticleDOI

Scalable molecular dynamics with NAMD

TL;DR: NAMD as discussed by the authors is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems that scales to hundreds of processors on high-end parallel platforms, as well as tens of processors in low-cost commodity clusters, and also runs on individual desktop and laptop computers.
Journal ArticleDOI

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

TL;DR: Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described in this article, where the rule of 5 is used to predict poor absorption or permeability when there are more than 5 H-bond donors, 10 Hbond acceptors, and the calculated Log P (CLogP) is greater than 5 (or MlogP > 415).
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