On Wiener Polarity Index and Wiener Index of Certain Triangular Networks

A topological index of graph G is a numerical quantity which describes its topology. If it is applied to the molecular structure of chemical compounds, it reflects the theoretical properties of the chemical compounds. A number of topological indices have been introduced so far by different researchers. The Wiener index is one of the oldest molecular topological indices defined by Wiener. The Wiener index number reflects the index boiling points of alkane molecules. Quantitative structure activity relationships (QSAR) showed that they also describe other quantities including the parameters of its critical point, density, surface tension, viscosity of its liquid phase, and the van der Waals surface area of the molecule. The Wiener polarity index has been introduced by Wiener and known to be related to the cluster coefficient of networks. In this paper, the Wiener polarity index W p G and Wiener index W G of certain triangular networks are computed by using graph-theoretic analysis, combinatorial computing, and vertex-dividing technology.

De-novo generation of novel phenotypically active molecules for Chagas disease from biological signatures using AI-driven generative chemistry

Designing novel molecules with targeted biological activities and optimized physicochemical properties is a challenging endeavor in drug discovery. Recent developments in artificial intelligence have enhanced the early steps of de novo drug design and compound optimization. Herein, we present a generative adversarial network trained to design new chemical matter that satisfies a given biological signature. Our model, called pqsar2cpd, is based on the activity of compounds across multiple assays obtained via pQSAR (profile-quantitative structure-activity relationships). We applied pqsar2cpd to Chagas disease and designed a novel molecule that was experimentally confirmed to inhibit growth of parasites in vitro at low micromolar concentrations. Altogether, this approach bridges chemistry and biology into one single framework for the design of novel molecules with promising biological activity.

The effect of noise on the predictive limit of QSAR models

A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed in the field of QSAR that models cannot produce predictions which are more accurate than their training data. Additionally, it is implicitly assumed, by necessity, that data points in test sets or validation sets do not contain error, and that each data point is a population mean. This work proposes the hypothesis that QSAR models can make predictions which are more accurate than their training data and that the error-free test set assumption leads to a significant misevaluation of model performance. This work used 8 datasets with six different common QSAR endpoints, because different endpoints should have different amounts of experimental error associated with varying complexity of the measurements. Up to 15 levels of simulated Gaussian distributed random error was added to the datasets, and models were built on the error laden datasets using five different algorithms. The models were trained on the error laden data, evaluated on error-laden test sets, and evaluated on error-free test sets. The results show that for each level of added error, the RMSE for evaluation on the error free test sets was always better. The results support the hypothesis that, at least under the conditions of Gaussian distributed random error, QSAR models can make predictions which are more accurate than their training data, and that the evaluation of models on error laden test and validation sets may give a flawed measure of model performance. These results have implications for how QSAR models are evaluated, especially for disciplines where experimental error is very large, such as in computational toxicology. Graphical Abstract

Design, Synthesis and Tumour-Selective Toxicity of Novel 1-[3-{3,5-Bis(benzylidene)-4-oxo-1-piperidino}-3-oxopropyl]-4-piperidone Oximes and Related Quaternary Ammonium Salts

A novel series of 1-[3-{3,5-bis(benzylidene)-4-oxo-1-piperidino}-3-oxopropyl]-4-piperidone oximes 3a–h and related quaternary ammonium salts 4a–h were prepared as candidate antineoplastic agents. Evaluation against neoplastic Ca9-22, HSC-2 and HSC-4 cells revealed the compounds in series 3 and 4 to be potent cytotoxins with submicromolar CC50 values in virtually all cases. In contrast, the compounds were less cytocidal towards HGF, HPLF and HPC non-malignant cells revealing their tumour-selective toxicity. Quantitative structure–activity relationships revealed that, in general, both cytotoxic potency and selectivity index figures increased as the magnitude of the Hammett sigma values rose. In addition, 3a–h are cytotoxic towards a number of leukemic and colon cancer cells. 4b,c lowered the mitochondrial membrane potential in CEM cells, and 4d induced transient G2/M accumulation in Ca9-22 cells. Five compounds, namely 3c,d and 4c–e, were identified as lead molecules that have drug-like properties.

3D-QSAR and Molecular Docking Approaches for the Identification of Phyto-Inhibitors of Hsp90

Inhibition of Hsp90 disrupts the Hsp90 client protein complex, resulting in its breakdown. Phytochemicals from reported anticancer plants were screened against the orthosteric site of Hsp90. The lead compounds were subjected to the Lipinski rule of five to evaluate their drug-likeness. Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR), a mathematical model for the inhibition of Hsp90, was also derived. The lead compounds are guaiol from Cannabis sativa, actinidine from Anacadium occidentale, and choline from Tinospora cordifolia with docking scores of -11kcal/mol, -12.1kcal/mol, and -10.8kcal/mol, respectively. The 3D-QSAR model generated is robust and thoroughly validated with a correlation coefficient R of 0.94 and R2 of 0.950. Actinidine, choline and, guaiol are novel and potent inhibitors of Hsp90. They form interactions with key amino acid residues within the Hsp90 orthosteric site.

Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors

Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for “old” drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure–activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL model exhibited an accuracy higher than 85%. A physicochemical and fragment-based structural interpretation of this model was provided, and a large dataset of agency-regulated chemicals was virtually screened, with the mtc-QSAR-EL model identifying already proven antituberculosis drugs while proposing chemicals with great potential to be experimentally repurposed as antituberculosis (multi-strain inhibitors) agents. Some of the most promising molecules identified by the mtc-QSAR-EL model as antituberculosis agents were also confirmed by another computational approach, supporting the capabilities of the mtc-QSAR-EL model as an efficient tool for computational drug repurposing.

2D-quantitative structure–activity relationships model using PLS method for anti-malarial activities of anti-haemozoin compounds

Background Emergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation. Methods With this in mind, this study developed two-dimensional-quantitative structure–activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers. Results Five two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in “ortho” position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity. Conclusions The model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain.