1,720,994 research outputs found
Prediction of Acute Oral Systemic Toxicity Using a Multifingerprint Similarity Approach
No full textThe implementation of nonanimal approaches is of particular importance to regulatory agencies for the prediction of potential hazards associated with acute exposures to chemicals. This work was carried out in the framework of an international modeling initiative organized by the Acute Toxicity Workgroup (ATWG) of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) with the participation of 32 international groups across government, industry, and academia. Our contribution was to develop a multifingerprints similarity approach for predicting five relevant toxicology endpoints related to the acute oral systemic toxicity that are: The median lethal dose (LD 50) point prediction, the "nontoxic" (LD 50 > 2000 mg/kg) and "very toxic" (LD 50 <50 mg/kg) binary classification, and the multiclass categorization of chemicals based on the United States Environmental Protection Agency and Globally Harmonized System of Classification and Labeling of Chemicals schemes. Provided by the ICCVAM's ATWG, the training set used to develop the models consisted of 8944 chemicals having high-quality rat acute oral lethality data. The proposed approach integrates the results coming from a similarity search based on 19 different fingerprint definitions to return a consensus prediction value. Moreover, the herein described algorithm is tailored to properly tackling the so-called toxicity cliffs alerting that a large gap in LD 50 values exists despite a high structural similarity for a given molecular pair. An external validation set made available by ICCVAM and consisting in 2896 chemicals was employed to further evaluate the selected models. This work returned high-Accuracy predictions based on the evaluations conducted by ICCVAM's ATWG
Challenging AQP4 druggability for NMO-IgG antibody binding using molecular dynamics and molecular interaction fields
Full text linkAbstractNeuromyelitis optica (NMO) is a multiple sclerosis-like immunopathology disease affecting optic nerves and the spinal cord. Its pathological hallmark is the deposition of a typical immunoglobulin, called NMO-IgG, against the water channel Aquaporin-4 (AQP4). Preventing NMO-IgG binding would represent a valuable molecular strategy for a focused NMO therapy. The recent observation that aspartate in position 69 (D69) is determinant for the formation of NMO-IgG epitopes prompted us to carry out intensive Molecular Dynamics (MD) studies on a number of single-point AQP4 mutants. Here, we report a domino effect originating from the point mutation at position 69: we find that the side chain of T62 is reoriented far from its expected position leaning on the lumen of the pore. More importantly, the strength of the H-bond interaction between L53 and T56, at the basis of the loop A, is substantially weakened. These events represent important pieces of a clear-cut mechanistic rationale behind the failure of the NMO-IgG binding, while the water channel function as well as the propensity to aggregate into OAPs remains unaltered. The molecular interaction fields (MIF)-based analysis of cavities complemented MD findings indicating a putative binding site comprising the same residues determining epitope reorganization. In this respect, docking studies unveiled an intriguing perspective to address the future design of small drug-like compounds against NMO. In agreement with recent experimental observations, the present study is the first computational attempt to elucidate NMO-IgG binding at the molecular level, as well as a first effort toward a less elusive AQP4 druggability
Morphological and charge transport properties of amorphous and crystalline P3HT and PBTTT: Insights from theory
No full textEffects of Different Self-Assembled Monolayers on Thin-Film Morphology: A Combined DFT/MD Simulation Protocol
No full textOrganic thin film transistors (OTFTs) are multilayer field-effect transistors that employ an organic
conjugated material as semiconductor. Several experimental groups have recently demonstrated that the insertion of an
organic self-assembled monolayer (SAM) between the dielectric and the semiconductive layer is responsible for a
sensible improvement of the OTFT performances in terms of an increased charge carrier mobility caused by a higher degree of order in the organic semiconductor layer. Here, we describe a combined periodic density functional theory (DFT) and classical molecular dynamics (MD) protocol applied to four different SAMs and a pentacene monolayer deposited onto their surfaces. In particular, we investigate the morphology and the surface of the four SAMs and the translational, orientational, and nematic order of the monolayer through the calculation of several distribution functions and order parameters pointing out the differences among the systems and relating them to known experimental results. Our calculations also suggest that small differences in the SAM molecular design will produce remarkable differences in the SAM surface and monolayer order. In particular, our simulations explain how a SAM with a bulky terminal group results in an irregular and rough surface that determines the deposition of a disordered semiconductive monolayer. On the contrary, SAMs with a small terminal group generate smooth surfaces with uninterrupted periodicity, thus favoring the formation of an ordered pentacene monolayer that increases the mobility of charge carriers and improves the overall performances of the OTFT devices. Our results clearly point out that the in silico procedure presented here might be of help in tuning the design of SAMs in order to improve the quality of OTFT devices
Permeability Coefficients of Lipophilic Compounds Estimated by Computer Simulations
No full textThe ability of a drug to cross the intestine–blood barrier is a key quantity for drug design and employment and is normally quantified by the permeability coefficient P, often evaluated in the so-called Caco-2 assay. This assay is based on measuring the initial growth rate of the concentration of the drug beyond the cellular barrier but not its steady-state flux through the membrane. This might lead to confusion since, in the case of lipophilic drugs, the initial slope is strongly affected by the retention of the drug in the membrane. This effect is well known but seldom considered in the assay. Here, we exploit all-atoms molecular dynamics and bias exchange metadynamics to calculate the concentration of two lipophilic drugs across a model membrane as a function of time. This allows estimating both the steady-state flux and the initial slope of the concentration growth and comparing Caco-2 and steady-state estimates of P. We show that our computational procedure is able to reproduce the experimental values, although these may differ from the permeability coefficients by orders of magnitude. Our findings are generalized by a simplified one-dimensional model of the permeation process that may act as a roadmap to assess which measure of membrane permeability would be more appropriate and, consequently, whether retention corrections should be included in estimates based on Caco-2 assays
SIGMAP: an explainable artificial intelligence tool for SIGMA-1 receptor affinity prediction
Full text linkDeveloping sigma-1 receptor (S1R) modulators is considered a valuable therapeutic strategy to counteract neurodegeneration, cancer progression, and viral infections, including COVID-19. In this context, in silico tools capable of accurately predicting S1R affinity are highly desirable. Herein, we present a panel of 25 classifiers trained on a curated dataset of high-quality bioactivity data of small molecules, experimentally tested as potential S1R modulators. All data were extracted from ChEMBL v33, and the models were built using five different fingerprints and machine-learning algorithms. Remarkably, most of the developed classifiers demonstrated good predictive performance. The best-performing model, which achieved an AUC of 0.90, was developed using the support vector machine algorithm with Morgan fingerprints. To provide additional, user-friendly information for medicinal chemists in the rational design of S1R modulators, two independent explainable artificial intelligence (XAI) approaches were employed, namely Shapley Additive exPlanations (SHAP) and Contrastive Explanation. The top-performing model is accessible through a user-friendly web platform, SIGMAP (https://www.ba.ic.cnr.it/softwareic/sigmap/), specifically developed for this purpose. With its intuitive interface, robust predictive power, and implemented XAI approaches, SIGMAP serves as a valuable tool for the rational design of new and more effective S1R modulators
ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators
Full text link: The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators
De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization
Full text linkArtificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information
Organic bioelectronics probing conformational changes in surface confined proteins
Full text linkThe study of proteins confined on a surface has attracted a great deal of attention due to its relevance in the development of bio-systems for laboratory and clinical settings. In this respect, organic bio-electronic platforms can be used as tools to achieve a deeper understanding of the processes involving protein interfaces. In this work, biotin-binding proteins have been integrated in two different organic thin-film transistor (TFT) configurations to separately address the changes occurring in the protein-ligand complex morphology and dipole moment. This has been achieved by decoupling the output current change upon binding, taken as the transducing signal, into its component figures of merit. In particular, the threshold voltage is related to the protein dipole moment, while the field-effect mobility is associated with conformational changes occurring in the proteins of the layer when ligand binding occurs. Molecular Dynamics simulations on the whole avidin tetramer in presence and absence of ligands were carried out, to evaluate how the tight interactions with the ligand affect the protein dipole moment and the conformation of the loops surrounding the binding pocket. These simulations allow assembling a rather complete picture of the studied interaction processes and support the interpretation of the experimental results
Human aquaporin-4 and molecular modeling: Historical perspective and view to the future
Full text linkAmong the different aquaporins (AQPs), human aquaporin-4 (hAQP4) has attracted the greatest interest in recent years as a new promising therapeutic target. Such a membrane protein is, in fact, involved in a multiple sclerosis-like immunopathology called Neuromyelitis Optica (NMO) and in several disorders resulting from imbalanced water homeostasis such as deafness and cerebral edema. The gap of knowledge in its functioning and dynamics at the atomistic level of detail has hindered the development of rational strategies for designing hAQP4 modulators. The application, lately, of molecular modeling has proved able to fill this gap providing a breeding ground to rationally address compounds targeting hAQP4. In this review, we give an overview of the important advances obtained in this field through the application of Molecular Dynamics (MD) and other complementary modeling techniques. The case studies presented herein are discussed with the aim of providing important clues for computational chemists and biophysicists interested in this field and looking for new challenges
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