Can ‘Bacterial-Metabolite-Likeness' Model Improve Odds of ‘in Silico' Antibiotic Discovery?

Abstract

'Inductive' QSAR descriptors have been used to develop the series of QSAR models enabling 'in silico' distinguishing between antimicrobial compounds, conventional drugs, and druglike substances. The constructed neural network-based models operating by 30 'inductive' parameters have been validated on an extensive set of 2686 chemical structures and resulted in up to 97% accurate separation of the three types of molecular activities. The demonstrated ability of 'inductive' parameters to adequately capture molecular features determining 'antibiotic-like' and 'druglike' potentials have been further utilized to construct a model of 'Bacterial-Metabolite-Likeness' (BML). The same 'inductive' descriptors have been used to train a neural network that could very accurately recognize substances involved into bacterial metabolism (that have been experimentally identified). When the developed model has been applied to the mixed set of antimicrobials, drugs, and druglike chemicals (not used for training the BML model), it exhibited a 2-5-fold recognition preference toward antimicrobial compounds compared to general drugs and an 18- to 45-fold preference when compared to a druglike substance (depending on the model stringency). These results illustrate immanent similarity between conventional antimicrobials and native bacterial metabolites and suggest that the developed BML model can be an effective classification tool for 'in silico' antibiotic studies.

MetMaxStruct: A Tversky-Similarity-Based Strategy for Analysing the (Sub)Structural Similarities of Drugs and Endogenous Metabolites

“…We and others have written before about the potential utility of understanding the “likeness” of individual molecules to those considered representative of particular classes, such as drug-likeness (e.g., Karakoc et al, 2006; Paolini et al, 2006; Bickerton et al, 2012), natural-product-likeness (Ertl et al, 2008; Jayaseelan et al, 2012), or indeed metabolite-likeness (e.g., Cherkasov, 2006; Gupta and Aires-De-Sousa, 2007; Dobson P. D. et al, 2009; Peironcely et al, 2011; Walters, 2012; O'Hagan and Kell, 2015c; O'Hagan et al, 2015). Clearly this depends on the nature of the encoding used, but, as we see here, it can also depend markedly on the metric of similarity, that can be varied via the Tversky α and β parameters.…”

Comparative Qsar Analysis of Bacterial, Fungal Plant and Human Metabolites

“…Softness_of_Most_Pos, Sum_Hardness, Sum_Neg_Hardness, Total_Neg_Softness, b_double, b_rotN, b_rotR, b_triple, chiral, rings, a_nN, a_nO, a_nS, FCharge, lip_don, KierFlex, a_base, vsa_acc, vsa_acid, vsa_base, vsa_don For more details on 'inductive' parameters see references [1][2][3][4][5], while the used conventional QSAR parameters can be accessed through the MOE program [16].…”

“…In a series of previous works we reported the use of our own 'inductive' and conventional 2D and 3D QSAR descriptors for creating binary QSAR models capable of recognizing various groups of substances including antimicrobial molecules and peptides [1,2], steroid-like compounds [3], human therapeutics, drug-like chemicals [4] as well as bacterial and human metabolites [4,5]. These binary QSAR classifiers allowed defining certain structural determinants of the studied groups and provided important insights into their positioning in chemical space.…”

İkili parçacık sürü optimizasyonu ve destek vektör makinelerinin hibrit kullanımı ile ilaç keşfi için özellik seçimi

“…Çalışmada ilaç ve ilaç olmayan moleküllerden oluşan tamamen ayrık iki veri seti kullanılmıştır. Söz konusu moleküller Cherkasov ve Murcia-Soler'in çalışmalarından elde edilerek, her bir moleküle ait açıklayıcı özellikler Molecular Operating Environment (MOE, 2006) programı ile hesaplanmıştır (Cherkasov, 2006;Murcia-Soler vd., 2003). Murcia-Soler veri seti BPSO-DVM ve PSO-DVM biçimindeki hibrit yaklaşım ile özellikleri seçmek için, seçilen bu özelliklerin sınıflandırma başarısına etkisini ölçmek için ise Cherkasov verisi kullanılmıştır.…”