二氧化钛:一个集成的工具,用于硅分子性质预测和基于纳米结构的建模。

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Nikoletta-Maria Koutroumpa, Maria Antoniou, Dimitra-Danai Varsou, Konstantinos D Papavasileiou, Nikolaos K Sidiropoulos, Christoforos Kyprianou, Andreas Tsoumanis, Haralambos Sarimveis, Iseult Lynch, Georgia Melagraki, Antreas Afantitis
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引用次数: 0

摘要

药物发现和材料设计的进步很大程度上依赖于对大量化合物数据集的计算机分析,以及通过计算方法对其性质和活性的准确评估。高效、可靠的分子性质预测对化学工业中合理的化合物设计至关重要。为了满足这一需求,我们开发了9个关键特性的预测模型,包括辛醇/水分配系数、水溶性、水中实验水合自由能、蒸汽压、沸点、细胞毒性、诱变性、血脑屏障渗透性和生物浓缩因子。这些模型显示出很高的预测准确性,并根据经合组织的测试指南进行了彻底的验证。这些模型通过Titania (https://enaloscloud.novamechanics.com/EnalosWebApps/titania/)无缝集成到Enalos云平台中,Titania是一个全面的基于网络的应用程序,旨在使高级计算工具的访问民主化。Titania具有直观,用户友好的界面,允许研究人员,无论计算专业知识,都可以轻松地使用模型来预测新化合物的性质。该平台能够做出明智的决策,并支持药物发现和材料设计的创新。我们渴望这个工具成为科学界的宝贵资源,提高性质和毒性预测的效率和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling.

Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular properties is crucial for rational compound design in the chemical industry. To address this need, we have developed predictive models for nine key properties, including the octanol/water partition coefficient, water solubility, experimental hydration free energy in water, vapor pressure, boiling point, cytotoxicity, mutagenicity, blood-brain barrier permeability, and bioconcentration factor. These models have demonstrated high predictive accuracy and have undergone thorough validation in accordance with OECD test guidelines. The models are seamlessly integrated into the Enalos Cloud Platform through Titania ( https://enaloscloud.novamechanics.com/EnalosWebApps/titania/ ), a comprehensive web-based application designed to democratize access to advanced computational tools. Titania features an intuitive, user-friendly interface, allowing researchers, regardless of computational expertise, to easily employ models for property prediction of novel compounds. The platform enables informed decision-making and supports innovation in drug discovery and material design. We aspire for this tool to become a valuable resource for the scientific community, enhancing both the efficiency and accuracy of property and toxicity predictions.

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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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