Interpretable Molecular Descriptors Research Articles

Exploring QSARs for inhibitory effect of a set of EGFR tyrosine kinase inhibitors by GA-MLR and molecular Docking simulations

In this study, EGFR as a target for the anticancer activity of a series of 113 inhibitors were taken from the literature. Current work aims to derive statistically robust and appropriately validated multiple QSAR models using easily interpretable molecular descriptors and molecular docking analysis. It will be simpler to identify important structural trends and how they relate to anticancer activity as a result. The MLR model has been used to suggest some novel compounds with improved activity. It has been demonstrated how the predicted compounds interact with the enzyme using the docking study. All predicted compounds were discovered to have several hydrogen bonds with the receptor and involve their bulky groups in strong steric interactions with specific places of the enzyme. The proposed compounds exhibit good pharmacokinetic properties, according to the analysis of their pharmacokinetic profiles.

Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata.

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC50 for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R2=0.72, Q2LOO=0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

In silico prediction of cutaneous penetration rate of some chemicals from their molecular structural descriptors

The cutaneous penetration rate of some hormones and organic solvents to the stratum corneum was modeled and estimated by using interpretable molecular descriptors. To develop quantitative structure–activity relationship (QSAR) models, the methods of multiple linear regression and artificial neural network were used. The results of this study indicated the ability of the developed QSAR models in the prediction of the dermal penetration rate of various chemicals from their calculated molecular descriptors.

Redox Biotransformation and Delivery of Anthracycline Anticancer Antibiotics: How Interpretable Structure-activity Relationships of Lethality Using Electrophilicity and the London Formula for Dispersion Interaction Work.

Quantum chemical methods and molecular mechanics approaches face a lot of challenges in drug metabolism study because of either insufficient accuracy, huge computational cost, or lack of clear molecular level pictures for building computational models. Low-cost QSAR methods can often be carried out, even though molecular level pictures are not well defined; however, they show difficulty in identifying the mechanisms of drug metabolism and delineating the effects of chemical structures on drug toxicity because a certain amount of molecular descriptors are difficult to be interpreted. In order to make a breakthrough of QSAR, mechanistically interpretable molecular descriptors were used to correlate with biological activity to establish structure-activity plots. The biological activity is the lethality of anthracycline anticancer antibiotics denoted as log LD50. The mechanistically interpretable molecular descriptors include electrophilicity and the mathematical function in the London formula for dispersion interaction. The descriptors were calculated using quantum chemical methods. The plots for electrophilicity, which is interpreted as redox reactivity of anthracyclines, can describe oxidative degradation for detoxification and reductive bioactivation for toxicity induction. The plots for the dispersion interaction function, which represents the attraction between anthracyclines and biomolecules, can describe efflux from and influx into the target cells of toxicity. The plots can also identify three structural scaffolds of anthracyclines that have different metabolic pathways, resulting in their different toxicity behavior. This structure-dependent toxicity behavior revealed in the plots can provide perspectives on drug design and drug metabolism study.

Development of selective QSAR models and molecular docking study for inhibitory activity of sulfonamide derivatives against carbonic anhydrase isoforms II and IX

In this study, we presented four linear quantitative structure-activity relationship (QSAR) models, which were developed from calculated molecular descriptors and experimental inhibition constants of 43 sulfonamide compounds measured against carbonic anhydrase (CA) II and IX isoforms. In the first two models, binding affinity of the compounds against CA II and IX isoforms were treated as independent variables, whereas in the remaining two models, taking affinity ratio (RpKi) and differences (ΔpKi) with respect to the two CA isoforms were treated as independent variables. Physically interpretable molecular descriptors involved in the obtained QSAR models allowed us to put forth some structural features, which played a critical role in binding affinity selectivity of the ligands with respect to the two CA isoforms. Furthermore, we applied AUTODOCK protocol for docking simulation of two highly selective ligands, 7c and 8d in binding to CA isoforms. The obtained results demonstrated that interaction of tail moiety of the ligand with Phe131 residue is an important factor for a ligand being highly selective for CA II binding.

QSAR analysis for 6-arylpyrazine-2-carboxamides as Trypanosoma brucei inhibitors

Human African trypanosomiasis (HAT) is prevalent in African countries, covering 37 countries, mostly sub-Saharan. A limited number of drugs are available to cure this neglected disease. In the present work, quantitative structure–activity (toxicity) relationships (QSA(T)R) analysis has been performed for a dataset of 54 6-arylpyrazine-2-carboxamides as Trypanosoma brucei inhibitors to identify the important structural features required for future optimization of lead candidates. The QSA(T)R models satisfy OECD guidelines and have high statistical robustness. The QSA(T)R models are based on easily interpretable molecular descriptors. The QSA(T)R models indicate that Trypanosoma brucei inhibitory activity of 6-arylpyrazine-2-carboxamides has correlation with the presence of N-sec-butylformamide and substituted benzene. The results could be beneficial for further optimization of 6-arylpyrazine-2-carboxamides as Trypanosoma brucei inhibitors. Some potential candidate molecules have been proposed.

Free and Open Source Chemistry Software in Research of Quantitative Structure-Toxicity Relationship of Pesticides

Pesticides are toxic chemicals aimed for the destroying pest on crops. Numerous data evidence about toxicity of pesticides on aquatic organisms. Since pesticides with similar properties tend to have similar biological activities, toxicity may be predicted from structure. Their structure feature and properties are encoded my means of molecular descriptors. Molecular descriptors can capture quite simple two-dimensional (2D) chemical structures to highly complex three-dimensional (3D) chemical structures. Quantitative structure-toxicity relationship (QSTR) method uses linear regression analyses for correlation toxicity of chemical with their structural feature using molecular descriptors. Molecular descriptors were calculated using open source software PaDEL and in-house built PyMOL plugin (PyDescriptor). PyDescriptor is a new script implemented with the commonly used visualization software PyMOL for calculation of a large and diverse set of easily interpretable molecular descriptors encoding pharmacophoric patterns and atomic fragments. PyDescriptor has several advantages like free and open source, can work on all major platforms (Windows, Linux, MacOS). QSTR method allows prediction of toxicity of pesticides without experimental assay. In the present work, QSTR analysis for toxicity of a dataset of mixtures of 5 classes of pesticides comprising has been performed.

Quantum-chemically-calculated mechanistically interpretable molecular descriptors for drug-action mechanism study – a case study of anthracycline anticancer antibiotics

Identification of drug-action mechanisms and understanding of chemical substituents affecting the anticancer activity of drugs are important for drug development.

Molecular properties determining unbound intracellular and extracellular brain exposure of CNS drug candidates.

In the present work we sought to gain a mechanistic understanding of the physicochemical properties that influence the transport of unbound drug across the blood-brain barrier (BBB) as well as the intra- and extracellular drug exposure in the brain. Interpretable molecular descriptors that significantly contribute to the three key neuropharmacokinetic properties related to BBB drug transport (Kp,uu,brain), intracellular accumulation (Kp,uu,cell), and binding and distribution in the brain (Vu,brain) for a set of 40 compounds were identified using partial least-squares (PLS) analysis. The tailoring of drug properties for improved brain exposure includes decreasing the polarity and/or hydrogen bonding capacity. The design of CNS drug candidates with intracellular targets may benefit from an increase in basicity and/or the number of hydrogen bond donors. Applying this knowledge in drug discovery chemistry programs will allow designing compounds with more desirable CNS pharmacokinetic properties.

In silico prediction of dermal penetration rate of chemicals from their molecular structural descriptors

The dermal penetration rate of some volatile and non-volatile organic compounds was estimated by quantitative structure–activity relationship approaches by using interpretable molecular descriptors. Linear and nonlinear models were developed using multiple linear regressions (MLR) and artificial neural network (ANN) methods. Robustness and reliability of the constructed MLR and ANN models were evaluated by using the leave-one-out cross-validation method, which produces the statistics of QMLR2=0.786, Q ANN2=0.833 for non-volatiles and QMLR2=0.639, Q ANN2=0.712 for volatile compounds. Furthermore, the chemical applicability domains of these models were determined via leverage approach. The results of this study indicated the ability of developed QSAR models in the prediction of dermal penetration rate of various chemicals from their calculated molecular descriptors.