Simplified Molecular Input Line Entry System Research Articles

MBLinhibitors.com, a Website Resource Offering Information and Expertise for the Continued Development of Metallo--Lactamase Inhibitors.

In an effort to facilitate the discovery of new, improved inhibitors of the metallo-β-lactamases (MBLs), a new, interactive website called MBLinhibitors.com was developed. Despite considerable efforts from the science community, there are no clinical inhibitors of the MBLs, which are now produced by human pathogens. The website, MBLinhibitors.com, contains a searchable database of known MBL inhibitors, and inhibitors can be searched by chemical name, chemical formula, chemical structure, Simplified Molecular-Input Line-Entry System (SMILES) format, and by the MBL on which studies were conducted. The site will also highlight a “MBL Inhibitor of the Month”, and researchers are invited to submit compounds for this feature. Importantly, MBLinhibitors.com was designed to encourage collaboration, and researchers are invited to submit their new compounds, using the “Submit” function on the site, as well as their expertise using the “Collaboration” function. The intention is for this site to be interactive, and the site will be improved in the future as researchers use the site and suggest improvements. It is hoped that MBLinhibitors.com will serve as the one-stop site for any important information on MBL inhibitors and will aid in the discovery of a clinically useful MBL inhibitor.

Drug-Target Interaction Prediction: End-to-End Deep Learning Approach.

The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Therefore, establishing effective computational methods is decisive to find new leads in a reasonable amount of time. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. In this paper, we present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein sequences (amino acid sequence) and compounds SMILES (Simplified Molecular Input Line Entry System) strings. These representations can be interpreted as features that express local dependencies or patterns that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions.

Bidirectional Molecule Generation with Recurrent Neural Networks.

Recurrent neural networks (RNNs) are able to generate de novo molecular designs using simplified molecular input line entry systems (SMILES) string representations of the chemical structure. RNN-based structure generation is usually performed unidirectionally, by growing SMILES strings from left to right. However, there is no natural start or end of a small molecule, and SMILES strings are intrinsically nonunivocal representations of molecular graphs. These properties motivate bidirectional structure generation. Here, bidirectional generative RNNs for SMILES-based molecule design are introduced. To this end, two established bidirectional methods were implemented, and a new method for SMILES string generation and data augmentation is introduced-the bidirectional molecule design by alternate learning (BIMODAL). These three bidirectional strategies were compared to the unidirectional forward RNN approach for SMILES string generation, in terms of the (i) novelty, (ii) scaffold diversity, and (iii) chemical-biological relevance of the computer-generated molecules. The results positively advocate bidirectional strategies for SMILES-based molecular de novo design, with BIMODAL showing superior results to the unidirectional forward RNN for most of the criteria in the tested conditions. The code of the methods and the pretrained models can be found at URL https://github.com/ETHmodlab/BIMODAL.

QSAR-Models, Validation, and IIC-Paradox for Drug Toxicity

Three kinds of drug toxicities are examined in this modeling analysis. These are: (i) toxicity of psychotropic drugs; (ii) cardiac toxicity; and (iii) drug carcinogenicity. Predictive models for the toxicity data are built up by the Monte Carlo technique. The simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structure. Quantitative structure – activity relationships (QSAR) developed here are mathematical functions of corresponding SMILES. The index of ideality of correlation was tested as a tool to improve predictive potential of these models.

DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing.

The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.

Use of the index of ideality of correlation to improve aquatic solubility model

Models for water solubility are built up using so-called correlation weights of fragments of simplified molecular input-line entry system (SMILES), 2D graph invariants, and hierarchy of rings present in molecules. The approach is based on Monte Carlo optimization of the molecular features. Two versions of the optimization were examined: (i) the traditional version; and (ii) the Index of Ideality of Correlation (IIC) version. The statistical quality of models obtained with use of the IIC is comparable with models built up with application of physico-chemical endpoints and quantum mechanics descriptors recently suggested in the literature.

Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor

In search of potent α-amylase inhibitors, herein we report the synthesis, molecular docking and QSAR study of some thiazole clubbed pyrazole hybrids (TCPH) i.e., 1-((1-phenyl-3-aryl-1H-pyrazole-4-yl)methylene)-2-(4-arylthiazole-2-yl)hydrazine (4a–4r) as an α-amylase inhibitors. Among the different analogues, compounds 4g and 4h were found to be most potent at 50 µg/mL with 89.15% and 88.42% of inhibition. The Monte Carlo optimisation method was applied to build robust QSAR models for the prediction of percentage inhibition of TCPH at different concentration with various statistical parameters. The Simplified Molecular Input Line Entry System (SMILES) was applied to symbolise the molecular structure, descriptor calculation and model development. The role of the index of ideality correlation (IIC) was also studied which revealed a model for split 3 as a leading model with best R2 i.e., 0.9198. The compound 4l at different concentration (TCPH11, TCPH29 and TCPH47) was outside the applicability domain (AD) for the developed QSAR models for split 4 only. The SMILES attributes at three different concentrations were also detected. These attributes are the promoters of percentage increase/decrease in inhibition efficiency of the inhibitors. The docking simulation of most active compounds (4g and 4h) were performed within the active site of Aspergillus oryzae α-amylase (PDB ID: 7TAA) to analyse the binding conformation and interactions responsible for their activity. As a result, it was found that the binding interactions found between 4g, 4h and α-amylase were similar to those responsible for α-amylase inhibition by acarbose.Communicated by Ramaswamy H. Sarma

Comprehensive ensemble in QSAR prediction for drug discovery

BackgroundQuantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject.ResultsThe proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/.ConclusionsWe propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.

QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors

The simplified molecular input line entry system (SMILES) is particularly suitable for high-speed machine processing, based on the Monte Carlo method using CORAL software. Quantitative structure-property relationships (QSPR) of critical temperatures have been established using a dataset of 165 diverse organic compounds employing hybrid optimal descriptors defined by graph and SMILES notation. External validation is one of the most important parts in the evaluation of model performance. However, previous models on the same dataset have poor predictive power in the external test set, or the authors had not done that check. In the present work, the predictive ability of model has been tested using external validation. The statistical quality of the three splits are similar and good. The r2 values for the best model are: r2 = 0.98 for the training set, r2 = 0.95 for the calibration set, and r2 = 0.94 for the validation set.

BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules.

Having a compact yet robust structurally based identifier or representation system is a key enabling factor for efficient sharing and dissemination of research results within the chemistry community, and such systems lay down the essential foundations for future informatics and data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. This is because, unlike other disciplines in chemistry, the basic premise that each distinct chemical species corresponds to a well-defined chemical structure does not hold for polymers. Polymers are intrinsically stochastic molecules that are often ensembles with a distribution of chemical structures. This difficulty limits the applicability of all deterministic representations developed for small molecules. In this work, a new representation system that is capable of handling the stochastic nature of polymers is proposed. The new system is based on the popular “simplified molecular-input line-entry system” (SMILES), and it aims to provide representations that can be used as indexing identifiers for entries in polymer databases. As a pilot test, the entries of the standard data set of the glass transition temperature of linear polymers (Bicerano, 2002) were converted into the new BigSMILES language. Furthermore, it is hoped that the proposed system will provide a more effective language for communication within the polymer community and increase cohesion between the researchers within the community.

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction.

Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: Given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. We show that a multihead attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark data set. Molecular Transformer makes predictions by inferring the correlations between the presence and absence of chemical motifs in the reactant, reagent, and product present in the data set. Our model requires no handcrafted rules and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without a reactant–reagent split and including stereochemistry, which makes our method universally applicable.

The index of ideality of correlation: models for flammability of binary liquid mixtures

Data on flammability of binary liquid mixtures are necessary to rational classification of different binary mixtures of liquids. List of corresponding binary mixtures, which have practical applications, is large and gradually, this list is increasing. Hence, reliable models for the endpoint can be useful. Simplified molecular input-line entry system (SMILES) is the representation of the molecular structure. The SMILES can be applied to build up quantitative structure—property/activity relationships (QSPRs/QSARs). Quasi-SMILES is the expansion of traditional SMILES by means of additional symbols that reflect “eclectic” conditions which able to influence physicochemical endpoints. The applying of the quasi-SMILES to build up model for flammability of binary liquid mixtures has indicated that the approach gives quite good model for the flash points (°C) of binary mixtures of organic substances. The index of ideality of correlation (IIC) is a new criterion of predictive potential. The attempts of applying of the IIC to improve models for flammability of binary liquid mixtures were successful.

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network

BackgroundUnderstanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.ResultsIn this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant (R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works (Ammad-Ud-Din et al., J Chem Inf Model 54:2347–9, 2014), (Haider et al., PLoS ONE 10:0144490, 2015), (Menden et al., PLoS ONE 8:61318, 2013). However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0.ConclusionsOur approach is able to predict the drug effects on cancer cell lines with high accuracy, and its performance remains stable with less but high-quality data, and with fewer features for the cancer cell lines. tCNNS can also solve the problem of outliers in other feature space. Besides achieving high scores in these statistical metrics, tCNNS also provides some insights into the phenotypic screening. However, the performance of tCNNS drops in the blind test.

Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin: Application of MARSplines and Descriptors Encoded in SMILES String

The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).

Idealization of correlations between optimal simplified molecular input-line entry system-based descriptors and skin sensitization

ABSTRACTThe Index of Ideality of Correlation (IIC) is a new criterion of the predictive potential for quantitative structure–property/activity relationships. The value of the IIC is a mathematical function sensitive to the value of the correlation coefficient and dispersion (expressed via mean absolute error). The IIC has been applied to develop QSAR models for skin sensitization achieving good predictive potential. The ‘ideal correlation’ is based on elementary fragments of simplified molecular input-line entry system (SMILES) and on the taking into account of the total numbers of nitrogen, oxygen, sulphur and phosphorus in the molecule.

The Correlation Contradictions Index (CCI): Building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES

The interpretation of the mutagenic potential of silver nanoparticles as a mathematical function of (i) dose; (ii) coating; and (iii) type of mutagenicity (TA98 and TA100) gives quantitative models with good statistical quality. So-called quasi-SMILES are used to represent examined objects (silver nanoparticles under different conditions) for building up models. Simplified molecular input-line entry systems (SMILES) is a well-known sequence of symbols for representation of the molecular structure. Quasi-SMILES is a similar sequence of symbols for representation of experimental conditions. The Correlation Contradiction Index (CCI) calculated with data on the calibration set gives possibility to predict quality of correlation of “experimental vs. calculated values of endpoint” for external validation set.

Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals

The quantitative activity relationships (QSARs) of the radical scavenging activities of 96 natural antioxidants based on the graph of atomic orbitals by CORAL software. The QSAR models are developed with the representation of the molecular structure by the Simplified Molecular Input-Line Entry System (SMILES) and the Monte Carlo optimization was used for calculation of the correlation weights of optimal SMILES based descriptors. The data set was randomly distributed three times into the training, invisible training, calibration and validation set was examined. The R2 values of the validation set for splits 1 to 3 were 0.966, 0.921, and 0.886 respectively. These models are established using only SMILES information, without any information on physicochemical parameters, the quantum mechanics descriptors, or 3D descriptors of the antioxidant structures. The obtained results indicated that the proposed QSAR models can be successfully used for predictions of the radical scavenging activities of new natural and synthetic antioxidants.

Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness.

The quantitative structure - activity relationships (QSARs) for sweetness value (log S) were built with a dataset of 315 molecules; following a novel criterion of 'Index of Ideality of Correlation(IIC)' This criterion of IIC is available in the latest version of the CORAL software (www.insilico.eu/coral). The descriptor used in the model building for log S is a hybrid optimal descriptor; obtained by combining the two descriptors: (i) molecular graph based descriptor derived from correlation weights of molecular features and (ii) descriptor derived from the simplified molecular input-line entry system (SMILES) code of sweetener molecule. The data set of 315 molecules was divided into four random splits. The four QSAR models which were build for log S using the criterion of IIC were compared with four similar models built "traditional protocol" described elsewhere. The comparison revealed that the models built using IIc were better with statistical performance.

Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks.

Several recent reports have shown that long short-term memory generative neural networks (LSTM) of the type used for grammar learning efficiently learn to write Simplified Molecular Input Line Entry System (SMILES) of druglike compounds when trained with SMILES from a database of bioactive compounds such as ChEMBL and can later produce focused sets upon transfer learning with compounds of specific bioactivity profiles. Here we trained an LSTM using molecules taken either from ChEMBL, DrugBank, commercially available fragments, or from FDB-17 (a database of fragments up to 17 atoms) and performed transfer learning to a single known drug to obtain new analogs of this drug. We found that this approach readily generates hundreds of relevant and diverse new drug analogs and works best with training sets of around 40,000 compounds as simple as commercial fragments. These data suggest that fragment-based LSTM offer a promising method for new molecule generation.

Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method

ABSTRACTFructose-1,6-bisphosphatase (FBPase) is an enzyme important for regulation of gluconeogenesis, which is a major process in the liver responsible for glucose production. Inhibition of FBPase enzyme causing blockage of the gluconeogenesis process represents a newer scheme in the progress of anti-diabetic drugs. The current research describes the development of hybrid optimal descriptors-based quantitative structure–activity relationship (QSAR) models intended for a set of 62 FBPase inhibitors with the Monte Carlo method. The molecular structures were expressed by the simplified molecular input line entry system (SMILES) notation. Three splits were prepared by random division of the molecules into training set, calibration set and validation set. Statistical parameters obtained from QSAR modelling were good for various designed splits. The best QSAR model showed the following parameters: the values of r2 for calibration set and validation set of the best model were 0.6837 and 0.8623 and of Q2 were 0.6114 and 0.8036, respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoter of the endpoint. Further, these structural attributes were used in designing of new FBPase inhibitors and a molecular docking study was completed for the determination of interactions of the designed molecules with the enzyme.