Three-class classification models of logS and logP derived by using GA–CG–SVM approach

Zhang, Hui; Xiang, Mingli; Ma, C; Huang, Qi; Li, Wei; Xie, Yang; Wei, Yuquan; Yang, Shengyong

doi:10.1007/s11030-009-9108-1

Cited by 22 publications

(20 citation statements)

References 32 publications

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“…Molecular fingerprints are widely applied in substructure/similarity searching,( 35 ) compound clustering,( 36 ) and classification. ( 22 ) In this study, considering both their popularity and public availability, we adopted the MDL MACCS key( 37 ) and the PubChem fingerprint. ( 38 ) The MACCS key is a binary vector of 166 structural and/or physicochemical features (MACCS166), while the PubChem fingerprint represents the presence/absence of 881 substructures (PC881).…”

Section: Methodsmentioning

confidence: 99%

“…We also considered one additional fingerprint consisting of six physicochemical properties (ADD6), which were previously found to be relevant to solubility modeling. 22 , 39 With respect to these physicochemical properties, data sets I and II are rather diverse (Figure 1 ). Regardless of the minimal and maximal values, both data sets have similar distributions with respect to most of these properties.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection

Cheng

Wang

et al. 2011

J. Chem. Inf. Model.

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Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46 000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection

Cheng

Wang

et al. 2011

J. Chem. Inf. Model.

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“…Researchers have adopted ML‐driven approaches to predict some key physicochemical properties, such as water solubility, membrane permeability, and lipophilicity. We provide a detailed description of each property and discuss the ML‐based techniques that specifically predict the water solubility, 206–210 membrane permeability, 211–213 and lipophilicity 214–219 in the Supporting Information. Although improved ML models have led to better prediction of molecular properties, the lack of standard criteria for performance evaluation has limited the progress.…”

Section: Ai/ml Applications In Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

188

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Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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“…In this study, kernel RBF is used to LSSVM classification because its ability to handled the high dimension data [2] and produce a good performance [12].…”

Section: Training Lssvm Using the Optimal Parametermentioning

confidence: 99%

Least Squares Support Vector Machines Parameter Optimization Based on Improved Ant Colony Algorithm for Hepatitis Diagnosis

Husain

Arisa

Rahayu

et al. 2017

Jurnal Ilmu Komputer dan Informasi

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Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for the 80-20% training-testing partition. Keywords: Classification, Least Squares Support Vector Machines, Improved Ant Colony Algorithm, Local Fisher Discriminant Analysis, Hepatitis Disease. AbstrakBanyak metode klasifikasi yang mampu mendiagnosa seorang pasien mengidap penyakit Hepatitis, salah satunya adalah menggunakan metode klasifikasi Least Squares Support Vector Machines (LSSVM). Terdapat dua parameter yang sangat berpengaruh pada LSSVM yaitu parameter kernel dan parameter regularisasi. Penentuan parameter optimal tersebut harus diperhatikan untuk mendapatkan akurasi klasifikasi yang tinggi pada LSSVM. Penelitian ini mengusulkan metode optimasi Improved Ant Colony Algorithm (IACA) dalam penentuan parameter optimal LSSVM untuk mendiagnosa penyakit Hepatitis. IACA membuat penyimpanan solusi untuk menjaga rute dari keseluruhan semut. Solusi yang disimpan adalah nilai parameter LSSVM. Ada 3 tahapan utama pada penelitian ini yaitu, dimensi dataset Hepatitis direduksi menggunakan metode Local Fisher Discriminant Analysis (LFDA), kemudian parameter optimal LSSVM dicari dengan metode optimasi IACA menggunakan data training, setelah itu data testing diklasifikasikan menggunakan parameter optimal LSSVM. Hasil uji coba menunjukkan bahwa metode yang diusulkan menghasilkan nilai akurasi yang tinggi (93,7%) pada partisi 80-20% training dan testing.

show abstract

Three-class classification models of logS and logP derived by using GA–CG–SVM approach

Cited by 22 publications

References 32 publications

Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection

Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Least Squares Support Vector Machines Parameter Optimization Based on Improved Ant Colony Algorithm for Hepatitis Diagnosis

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