Effect of the integration method on the accuracy and computational efficiency of free energy calculations using thermodynamic integration

Although calculations of free energy using molecular dynamics simulations have gained significant importance in the chemical and biochemical fields, they still remain quite computationally intensive. Furthermore, when using thermodynamic integration, numerical evaluation of the integral of the Hamiltonian with respect to the coupling parameter may introduce unwanted errors in the free energy. In this paper, we compare the performance of two numerical integration techniques-the trapezoidal and Simpson's rules and propose a new method, based on the analytic integration of physically based fitting functions that are able to accurately describe the behavior of the data. We develop and test our methodology by performing detailed studies on two prototype systems, hydrated methane and hydrated methanol, and treat Lennard-Jones and electrostatic contributions separately. We conclude that the widely used trapezoidal rule may introduce systematic errors in the calculation, but these errors are reduced if Simpson's rule is employed, at least for the electrostatic component. Furthermore, by fitting thermodynamic integration data, we are able to obtain precise free energy estimates using significantly fewer data points (5 intermediate states for the electrostatic component and 11 for the Lennard-Jones term), thus significantly decreasing the associated computational cost. Our method and improved protocol were successfully validated by computing the free energy of more complex systems hydration of 2-methylbutanol and of 4-nitrophenol-thus paving the way for widespread use in solvation free energy calculations of drug molecules

A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands

10.1371/journal.pone.0039076PLoS ONE76

Support Vector Machine (SVM) as Alternative Tool to Assign Acute Aquatic Toxicity Warning Labels to Chemicals

Quantitative structure-activity relationship (QSAR) analysis has been frequently utilized as a computational tool for the prediction of several eco-toxicological parameters including the acute aquatic toxicity. In the present study, we describe a novel integrated strategy to describe the acute aquatic toxicity through the combination of both toxicokinetic and toxicodynamic behaviors of chemicals. In particular, a robust classification model (TOXclass) has been derived by combining Support Vector Machine (SVM) analysis with three classes of toxicokinetic\u2013like molecular descriptors: the autocorrelation molecular electrostatic potential (autoMEP) vectors, Sterimol topological descriptors and logP(o/w) property values. TOXclass model is able to assign chemicals to different levels of acute aquatic toxicity, providing an appropriate answer to the new regulatory requirements. Moreover, we have extended the above mentioned toxicokinetic-like descriptor set with a more toxicodynamic-like descriptors, as for example HOMO and LUMO energies, to generate a valuable SVM classifier (MOAclass) for the prediction of the mode of action (MOA) of toxic chemicals. As preliminary validation of our approach, the toxicokinetic (TOXclass) and the toxicodynamic (MOAclass) models have been applied in series to inspect both aquatic toxicity hazard and mode of action of 296 chemical substances with unknown or uncertain toxicodynamic information to assess the potential ecological risk and the toxic mechanism

PCA-Based Representations of Graphs for Prediction in QSAR Studies

In recent years, more and more attention has been paid on learning in structured domains, e.g. Chemistry. Both Neural Networks and Kernel Methods for structured data have been proposed. Here, we show that a recently developed technique for structured domains, i.e. PCA for structures, permits to generate representations of graphs (specif- ically, molecular graphs) which are quite effective when used for predic- tion tasks (QSAR studies). The advantage of these representations is that they can be generated automatically and exploited by any tradi- tional predictor (e.g., Support Vector Regression with linear kernel)

Comparison of Multilabel and Single-Label Classification Applied to the Prediction of the Isoform Specificity of Cytochrome P450 Substrates

Each drug can potentially be metabolized by different CYP450 isoforms. In the development of new drugs, the prediction of the metabolic fate is important to prevent drug-drug interactions. In the present study, a collection of 580 CYP450 substrates is deeply analyzed by applying multi- and single-label classification strategies, after the computation and selection of suitable molecular descriptors. Cross-training with support vector machine, multilabel k-nearest-neighbor and counterpropagation neural network modeling methods were used in the multilabel approach, which allows one to classify the compounds simultaneously in multiple classes. In the single-label models, automatic variable selection was combined with various cross-validation experiments and modeling techniques. Moreover, the reliability of both multi- and single-label models was assessed by the prediction of an external test set. Finally, the predicted results of the best models were compared to show that, even if the models present similar performances, the multilabel approach more coherently reflects the real metabolism information

Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study

Nowadays, in medicinal chem. adenosine receptors represent some of the most studied targets, and there is growing interest on the different adenosine receptor (AR) subtypes. The AR subtypes selectivity is highly desired in the development of potent ligands to achieve the therapeutic success. So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. In the present study, we have carried out a novel application of the multilabel classification approach by combining our recently reported autocorrelated mol. descriptors encoding for the mol. electrostatic potential (autoMEP) with support vector machines (SVMs). Three valuable models, based on decreasing thresholds of potency, have been generated as in series quant. sieves for the simultaneous prediction of the hA1R, hA2AR, hA2BR, and hA3R subtypes potency profile and selectivity of a large collection, more than 500, of known inverse agonists such as xanthine, pyrazolo-triazolo-pyrimidine, and triazolo-pyrimidine analogs. The robustness and reliability of our multilabel classification models were assessed by predicting an internal test set. Finally, we have applied our strategy to 13 newly synthesized pyrazolo-triazolo-pyrimidine derivs. inferring their full adenosine receptor potency spectrum and hAR subtypes selectivity profil

Exploring potency and selectivity receptor antagonist profiles using a multilabe classification approach: the human adenosine receptor as a key study

Nowadays, in medicinal chemistry adenosine receptors represent some of the most studied targets, and there is growing interest on the different adenosine receptor (AR) subtypes. The AR subtypes selectivity is highly desired in the development of potent ligands to achieve the therapeutic success. So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. In the present study, we have carried out a novel application of the multilabel classification approach by combining our recently reported autocorrelated molecular descriptors encoding for the molecular electrostatic potential (autoMEP) with support vector machines (SVMs). Three valuable models, based on decreasing thresholds of potency, have been generated as in series quantitative sieves for the simultaneous prediction of the hA(1)R, hA(2A)R, hA(2B)R, and hA(3)R subtypes potency profile and selectivity of a large collection, more than 500, of known inverse agonists such as xanthine, pyrazolo-triazolo-pyrimidine, and triazolo-pyrimidine analogues. The robustness and reliability of our multilabel classification models were assessed by predicting an internal test set. Finally, we have applied our strategy to 13 newly synthesized pyrazolo-triazolo-pyrimidine derivatives inferring their full adenosine receptor potency spectrum and hAR subtypes selectivity profile