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PD-L1 is a key immunotarget involved in binding to its receptor PD-1. PD-L1/PD-1 interface blocking using antibodies (or small molecules) is the central area of interest for tumor suppression in various cancers. Blocking the PD-L1/PD-1 pathway in the tumor cells results in its immune activation and destruction, and thereby restoring the T-cell proliferation and cytokine production. The active binding site interface residues of PD-L1/PD-1 were experimentally known and proven by structural biology and site-directed mutagenesis studies. Structure-based molecular design technique was employed to identify the inhibitors for blocking the PD-L1/PD-1 interface. Nine hits to leads were identified from the SPECS small molecule database by machine learning, molecular docking, and molecular dynamics simulation techniques. Following this, a machine learning-assisted QSAR modeling approach was implemented using ChEMBL database to gain insights into the inhibitory potential of PD-L1 inhibitors and predict the activity of our previously screened nine hit molecules. The best leads identified in the present study bind strongly with the active sites of PD-L1/PD-1 interface residues, which include A121, M115, I116, S117, I54, Y56, D122, and Y123. These computational leads are considered promising molecules for further in vitro and in vivo analysis to be developed as potential PD-L1 checkpoint inhibitors to cure different types of cancers. [ABSTRACT FROM AUTHOR]

A myriad of tobacco-associated chemicals may have possibilities to developmental/reproductive axis and endocrine-disruption impacts. Mostly they breach the biotransformation of cholesterol in mitochondria by interfering with steroidogenic pathway genes, prompting to adverse effects in steroid biosynthesis; however, studies are scanty. The quantitative structure–activity relationship (QSAR) modeling and comparative docking strategies were used to understand structural features of dataset compounds that influence developmental/reproductive toxicity and estrogen and androgen receptor–binding abilities, and to predict binding levels of toxicants with steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1) active sites. Developed QSAR models presented good robustness and predictive ability that were determined from the applicability domain and, clustering and classification of chemicals by performing self-organizing maps. Accordingly, the exorbitant amount of polycyclic aromatic hydrocarbons (PAHs) and a limited number of other chemicals including N-nitrosamines and nicotine was represented as potential developmental/reproductive toxicants as well as estrogen and androgen receptor binders. From the docking analysis, hydrogen bonding, nonpolar, atomic π-stacking, and π-cation interactions were found between PAHs (bay and fjord structural pockets) and functional hotspot residues of StAR and CYP11A1, which strengthened the subtle structural changes at domains. These govern barrier effects to cholesterol binding and/or locking cholesterol to complicate its ejection from the Ω1 loop of StAR, and further mitigates steroid biosynthesis through cholesterol by CYP11A1; therefore, they are presumably considered as block-cluster mechanisms. These outcomes are significant to be hopeful to estimate developmental/reproductive toxicity and endocrine-disruption activities of other environmental pollutants, and could be useful for further assessment to discover binding mechanisms of PAHs with other steroidogenesis pathway genes. [ABSTRACT FROM AUTHOR]

Antioxidative peptides derived from food proteins have been screened and characterized in in vitro assays, and several quantitative structure-activity relationship (QSAR) models have been established to understand the structure-activity relationship of the selected antioxidant peptides. However, a systematic evaluation of each fragment that could be produced by a certain protein is still lacking. In this study, all the tripeptides that could possibly derive from bovine beta-lactoglobulin based on its amino acid sequence have been synthesized, and their antioxidant activities were measured using a ferric-reducing antioxidant power assay. Three of the peptides (i.e., LTC, CQC, and GTW) exhibited higher activities than that of glutathione. A QSAR model was also computed using multiple linear regression with divided physicochemical property scores descriptors, and the validity of the model was confirmed by cross-validation and Y-scrambling. The result of the QSAR modeling indicated that the electronic and hydrogen-bonding properties of the amino acids in the tripeptide sequences, as well as the steric properties of the amino acid residues at the C- and N-termini, played an important role in the antioxidant activities of the tripeptides. The antioxidant activities of the tripeptides were generally higher with Cys and Trp amino acid residues in the sequence. The structural characteristics we found may contribute to the further research of antioxidative mechanism. [ABSTRACT FROM AUTHOR]

The structure-acute-toxicity relationship of organic acids and their derivatives was studied. The effect of the structure of 88 organic compounds on the acute toxicity upon i.p. administration in mice was studied using 2D simplex representation of the molecular structure and partial least-squares methods. Satisfactory quantitative structure-activity relationship (QSAR) models were constructed and analyzed to reveal the structural fragments that enhance toxicity. [ABSTRACT FROM AUTHOR]

Predictability and prediction reliability are of utmost important to characterize a good Quantitative structure-activity relationships (QSAR) model. However, validation methods are insufficient to guarantee the prediction reliability of QSAR models. Moreover, high dimensional samples also pose great challenge to traditional methods in terms of predictive power. Therefore, this study presents a predictive classifier (i.e., TreeEC) that can assess prediction reliability with high confidence, especially for facing high dimensional QSAR data. Two approaches for assessing prediction reliability are provided, i.e., applicability domain and prediction confidence. We demonstrate that the applicability domain has difficulty to guarantee the models' prediction reliability, where samples intensively close to the domain center are often poor predicted than those outside the domain. Instead, prediction confidence is more promising for assessing prediction reliability. Based on a large data set assessed by prediction confidence, external samples assessed with high confidence greater than 95 % can be reliably predicted with an accuracy of 94 %, in contrast to the average accuracy of 84 %. We also illustrate that TreeEC are less affected by high dimensionality than other popular methods according to 11 public data sets. A free version of TreeEC with a user-friendly interface can also be downloading from website . [ABSTRACT FROM AUTHOR]

In this article, we present an automatic model generation process for building QSAR models using Gaussian Processes, a powerful machine learning modeling method. We describe the stages of the process that ensure models are built and validated within a rigorous framework: descriptor calculation, splitting data into training, validation and test sets, descriptor filtering, application of modeling techniques and selection of the best model. We apply this automatic process to data sets of blood–brain barrier penetration and aqueous solubility and compare the resulting automatically generated models with ‘manually’ built models using external test sets. The results demonstrate the effectiveness of the automatic model generation process for two types of data sets commonly encountered in building ADME QSAR models, a small set of in vivo data and a large set of physico-chemical data. [ABSTRACT FROM AUTHOR]

A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC50 using chemical topological descriptors (calculated with the MolConnZ program) and variable selection k nearest neighbor ( kNN) method. Several validation protocols have been applied to achieve robust QSAR models. The original dataset was divided into multiple training and test sets, and the models were considered acceptable only if the leave-one-out cross-validated R 2 ( q 2) values were greater than 0.5 for the training sets and the correlation coefficient R 2 values were greater than 0.6 for the test sets. Furthermore, the q 2 values for the actual dataset were shown to be significantly higher than those obtained for the same dataset with randomized target properties (Y-randomization test), indicating that models were statistically significant. Ten best models were then employed to mine a commercially available ChemDiv Database (ca. 500 K compounds) resulting in 34 consensus hits with moderate to high predicted activities. Ten structurally diverse hits were experimentally tested and eight were confirmed active with the highest experimental EC50 of 1.8 μM implying an exceptionally high hit rate (80%). The same ten models were further applied to predict EC50 for four new PBTs, and the correlation coefficient ( R 2) between the experimental and predicted EC50 for these compounds plus eight active consensus hits was shown to be as high as 0.57. Our studies suggest that the approach combining validated QSAR modeling and virtual screening could be successfully used as a general tool for the discovery of novel biologically active compounds. [ABSTRACT FROM AUTHOR]

PGHS-1 and PGHS-2 are the targets of nonsteroidal anti- inflammatory drugs (NSAIDs). It appears that the high degree of selectivity for inhibition of PGHS-2 shown by certain compounds is the result of two mechanisms (time-dependent and time-independent inhibition), by which they interact with each isoform. The fenamic acids can be divided into competitive inhibitors of substrate binding and competitive inhibitors that cause time-dependent losses of cyclooxygenase activity. The cyclooxygenase activity was measured by oxygen consumption following preincubation of the enzyme and the inhibitor for increasing periods of time. The rate constants associated with binding inhibition kinetics and structure-activity relationships were calculated for a large number of fenamates, diclofenac and indomethacin. The KI* values are similar but the individual rate constants are markedly different: KI is two-fold lower, and k2 is six-fold slower for diclofenac than for indomethacin. All the active time-dependent compounds show MEPs with a negative conical surface, with their vertex on the minimum of the carboxyl group, which extends around the first aromatic ring to the central region. The conical surface keeps an open angle of 61° or larger, and a close contact surface with the residues Ala527, Ileu523, Val349, and Ser530, in the zones surrounding the bridging amino group and the chlorine atoms for meclofenamate and diclofenac, or in the region around the carbonyl group for indomethacin. The KI* and IC50 values indicate that the interactions that promote the slow binding kinetics must be examined in relation to the reaction energies of formation (ΔHr) of an ionic bond between the deprotonated carboxylic acid group of acid NSAIDs with the monocationic guanidinum group of Arg120, the free energies of solvation in aqueous solution, and the molecular volumes measured. Presumably indomethacin, diclofenac and meclofenamate cause the enzyme to undergo a subtle conformational change to a form that binds compounds even more tightly, with some slight structural changes confined to reorientations of the Arg277 and Gln358 side chains. These results show that the model has reliably chosen regions of biological significance consistent with both the X-ray crystallographic and kinetic results. [ABSTRACT FROM AUTHOR]