A random forest model for predicting the crystallisability of organic molecules

被引：21

作者：

Bhardwaj, Rajni M. ^{[1
]}

Johnston, Andrea ^{[1
]}

Johnston, Blair F. ^{[1
]}

Florence, Alastair J. ^{[1
]}

机构：

[1] Univ Strathclyde, Strathclyde Inst Pharm & Biomed Sci, Glasgow G4 0RE, Lanark, Scotland

来源：

CRYSTENGCOMM | 2015年 / 17卷 / 23期

基金：

英国工程与自然科学研究理事会;

关键词：

PROTEIN CRYSTALLIZATION PROPENSITY; CLASSIFICATION; QSAR; PURIFICATION; SCALE;

D O I：

10.1039/c4ce02403f

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

A random forest model has for the first time enabled the prediction of the crystallisability (crystals vs. no crystals) of organic molecules with similar to 70% accuracy. The predictive model is based on calculated molecular descriptors and published experimental crystallisation propensities of a library of substituted acylanilides.

引用

页码：4272 / 4275

页数：4

共 50 条

[21] A random forest based computational model for predicting novel lncRNA-disease associations
Dengju Yao
Xiaojuan Zhan
Xiaorong Zhan
Chee Keong Kwoh
Peng Li
Jinke Wang
BMC Bioinformatics, 21
[22] A Random Forest Regression Model for Predicting the Movement of Horseshoe Crabs in Long Island Sound
Senbel, Samah
Kasinak, Jo-Marie Elisha
Mattei, Jennifer
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT IV, 2021, 12952 : 107 - 119
[23] PREDICTING THE OUTCOMES OF IN VITRO FERTILIZATION PROGRAMS USING A RANDOM FOREST MACHINE LEARNING MODEL
Vladimirsky, G. M.
Zhuravleva, M. A.
Dashieva, A. E.
Korneeva, I. E.
Nazarenko, T. A.
BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY, 2023, (06): : 64 - 70
[24] Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model
Brokamp, Cole
Jandarov, Roman
Hossain, Monir
Ryan, Patrick
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2018, 52 (07) : 4173 - 4179
[25] Random forest prediction model for the soil organic matter with optimized spectral inputs
Zhang X.
Meng X.
Tang H.
Liu H.
Zhang X.
Liu Q.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2023, 39 (02): : 90 - 99
[26] Distributed Random Forest for Predicting Forest Wildfires Based on Weather Data
Damasevisius, Robertas
Maskeliunas, Rytis
ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2023, PT II, 2024, 2091 : 305 - 320
[27] Lasing and random lasing based on organic molecules
Sznitko, Lech
Cyprych, Konrad
Szukalski, Adam
Miniewicz, Andrzej
Rau, Ileana
Kajzar, Francois
Mysliwiec, Jaroslaw
OPTICS AND PHOTONICS FOR COUNTERTERRORISM, CRIME FIGHTING AND DEFENCE IX; AND OPTICAL MATERIALS AND BIOMATERIALS IN SECURITY AND DEFENCE SYSTEMS TECHNOLOGY X, 2013, 8901
[28] A random forest approach for predicting coal spontaneous combustion
Lei, Changkui
Deng, Jun
Cao, Kai
Ma, Li
Xiao, Yang
Ren, Lifeng
FUEL, 2018, 223 : 63 - 73
[29] Predicting Bank Financial Failures using Random Forest
Rustam, Zuherman
Saragih, Glori Stephani
2018 INTERNATIONAL WORKSHOP ON BIG DATA AND INFORMATION SECURITY (IWBIS), 2018, : 81 - 86
[30] Predicting degradation of organic molecules in cementitious media
Bagaria, F.
Riba, O.
Albrecht, A.
Robinet, J-C
Made, B.
Roman-Ross, G.
PROGRESS IN NUCLEAR ENERGY, 2021, 140

← 1 2 3 4 5 →