Search Results - "quantitative structure-property relationship"

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set

Zhao, Jiaxi^aff1, Hermans, Eline^aff2, Sepassi, Kia^aff3, Tistaert, Christophe^aff2, Bergström, Christel A. S.^aff1, Ahmad, Mazen^aff4, Larsson, Per^aff1

Compound Screening and Prioritization in Drug Discovery Using Machine Learning

Mariyam, A. Asiya, Varshni, R. R., Samyuktha, S. P., Kumar, V. Anush, Narmadha, B.

2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Kehrein, Josef^{aff1, aff2}, Bunker, Alex^aff2, Luxenhofer, Robert^aff1

Thermal Risk Assessment and Optimization of Hazard Ranking for Aromatic Nitro Compounds: Multiparameterization and Quantitative Structure–Property Relationship Modeling

Qin, Chuanrui^aff1, Dang, Mengtao^aff3, Meng, Yifei^aff2, Zhao, Dongfeng^{aff1, aff2, aff4}

Computational Modelling for Formulation Design

Ponduri, Teja Kumar^Aff3, Budha, Roja Rani^Aff4, Paliwal, Deepika^Aff5, Siripurapu, Kartheek^Aff6, Alavala, Rajasekhar Reddy^Aff7, Rao G., S. N. Koteswara^Aff7

Rao G., S.N. Koteswara, editor^Aff1, Alavala, Rajasekhar Reddy, editor^Aff2

In Silico QSPR Studies Based on CDFT and IT Descriptors

Poddar, Arpita^Aff4, Chattaraj, Pratim Kumar^Aff5

Leszczynski, Jerzy, Series Editor^Aff1, Roy, Kunal, editor^Aff2, Banerjee, Arkaprava, editor^Aff3

Machine Learning Models to Study Electronic Properties of Metal Nanoclusters

Kieu, Gia Minh^Aff4, Madridejos, Jenica Marie L.^Aff5, Lu, Yunpeng^Aff4

Leszczynski, Jerzy, Series Editor^Aff1, Roy, Kunal, editor^Aff2, Banerjee, Arkaprava, editor^Aff3

Predictive modeling of visible-light azo-photoswitches’ properties using structural features

Said Byadi, P. K. Hashim, Pavel Sidorov

electronic resource

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Best Practices for Constructing Reproducible QSAR Models

Nantasenamat, Chanin^Aff3

Roy, Kunal^Aff2, Kang, Y. James^Aff1

Discovery of novel fluorophores in the human organism with quantitative structure-property relationship approach

Yakimov, B.P., Rubekina, A.A., Urusova, L.S., Shirshin, E.A.

2024 International Conference Laser Optics (ICLO)

The q-RASPR approach for predicting the property and fate of persistent organic pollutants

Chia Ming Chang, Arkaprava Banerjee, Vinay Kumar, Kunal Roy, Emilio Benfenati

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The Insulation Strength Structure-Property Relationship Model of Gas Containing Double or Triple Bonds

Zhao, Q., Tong, D., Tu, Y., Zheng, Z., Qiu, X., Wang, C.

Prediction of the Solubility of Acid Gas Hydrogen Sulfide in Green Solvent Ionic Liquids via Quantitative Structure–Property Relationship Models Based on the Molecular Structure

Liu, Tianxiong, Dong, Zihao, Zhu, Wenguang, Chen, Yusen, Zhou, Mengjin, Cui, Peizhe, Wang, Yinglong, Zhu, Zhaoyou

Machine learning assisted Q×f value prediction of ABO4-type microwave dielectric ceramics

Liangyu Mo, Jincheng Qin, Mingsheng Ma, Zhifu Liu

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Predicting Bile and Lipid Interaction for Drug Substances

Schlauersbach, Jonas^aff1, Kehrein, Josef^aff1, Hanio, Simon^aff1, Galli, Bruno^aff2, Harlacher, Cornelius^aff2, Heidenreich, Christopher^aff1, Lenz, Bettina^aff1, Sotriffer, Christoph^aff1, Meinel, Lorenz^{aff1, aff3}

Extractive distillation of cycloalkane monomers from the direct coal liquefaction fraction

Zhang, Shuo-Shuo^{Aff1, Aff2}, Wang, Xing-Bao^{Aff1, Aff2, IDs1170502424825_cor2}, Li, Wen-Ying^{Aff1, IDs1170502424825_cor3}

Application of deep eutectic solvents in lubrication: A review

Sernaglia, M. ^a, Bartolomé, M. ^a, Viesca, J.L. ^b, González, R. ^a, Battez, A. Hernández ^{b, ⁎}

Visualizing molecular structure—Adsorption efficiency relationship through an interpretable machine learning strategy

Li, Shaowen ^{a, 1}, Mao, Xinyu ^{b, 1}, Cao, Xiaoqin ^a, Feng, Yujun ^a, Zhang, Yongqing ^{b, ⁎}, Yin, Hongyao ^{a, ⁎}

A full in-silico approach for quantifying the effect of intramolecular hydrogen bonds on physicochemical properties

Zhou, Yufang ^a, Ruan, Qiyang ^a, Li, Yun ^{a, b}, Fan, Yulan ^{a, ⁎}, Chen, Deliang ^{a, ⁎}

An interpretable surrogate model for H2S solubility forecasting in ionic liquids based on machine learning

He, Yanjiang ^{a, b}, Yang, Ao ^{c, ⁎}, Zou, Changjun ^a, Fan, Tianyou ^d, Lan, Qikui ^d, He, Yu ^d, Wang, Meng ^d, Sunarso, Jaka ^e, Kong, Zong Yang ^{e, f, ⁎⁎}