Abstract
QSAR (Quantitative Structure Activity Relationships) models for the prediction of human intestinal absorption (HIA) were built with molecular descriptors calculated by ADRIANA.Code, Cerius2 and a combination of them. A dataset of 552 compounds covering a wide range of current drugs with experimental HIA values was investigated. A Genetic Algorithm feature selection method was applied to select proper descriptors. A Kohonen's self-organizing Neural Network (KohNN) map was used to split the whole dataset into a training set including 380 compounds and a test set consisting of 172 compounds. First, the six selected descriptors from ADRIANA.Code and the six selected descriptors from Cerius2 were used as the input descriptors for building quantitative models using Partial Least Square (PLS) analysis and Support Vector Machine (SVM) Regression. Then, another two models were built based on nine descriptors selected by a combination of ADRIANA.Code and Cerius2 descriptors using PLS and SVM, respectively. For the three SVM models, correlation coefficients (r) of 0.87, 0.89 and 0.88 were achieved; and standard deviations (s) of 10.98, 9.72 and 9.14 were obtained for the test set.
Highlights
In drug discovery and development process, complexity and risk have increased greatly as they have become more expensive and time-consuming
Fifty five descriptors were calculated by ADRIANA.Code. they include: molecular weight (MW), Topological Polar Surface Area (TPSA) [27], aqueous solubility [17, 28, 29], octanol/water partition coefficient (XlogP) [30], number of violations of the rule of 5 (Nrule5) [31], number of H-bond donor groups (Hdon), number of H-bond acceptor groups (Hacc), 2D molecular autocorrelation vectors et al In the autocorrelation vectors calculated by ADRIANA.Code, the hydrogen atoms were included
Six descriptors were selected from the initial 55 descriptors calculated by ADRIANA.Code after the genetic-Partial Least Square (PLS) feature selection, which are Nrule5, number of rotatable bonds (Nrot), MW, LogS, TPSA and Acorr_Sigchg_3
Summary
In drug discovery and development process, complexity and risk have increased greatly as they have become more expensive and time-consuming. Hundreds of millions of dollars and several years are required to develop a new drug. Some drugs fail to recover their research and development costs. Market withdrawals add to the industry’s problems. The attrition of compounds through clinical development means that only one in ten compounds entering development will ever make it to the marketplace [1]. The main cause for high attrition rates in drug discovery is from the absorption, distribution, metabolism and excretion (ADME) properties of candidate compounds. Many active drugs fail in phase II or III of the clinical development process because they do not reach their intended target.
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