A generalizable definition of chemical similarity for read-across

Background: Methods that provide a measure of chemical similarity are strongly relevant in several fields of chemoinformatics as they allow to predict the molecular behavior and fate of structurally close compounds. One common application of chemical similarity measurements, based on the principle that similar molecules have similar properties, is the read-across approach, where an estimation of a specific endpoint for a chemical is provided using experimental data available from highly similar compounds. Results: This paper reports the comparison of multiple combinations of binary fingerprints and similarity metrics for computing the chemical similarity in the context of two different applications of the read-across technique. Conclusions: Our analysis demonstrates that the classical similarity measurements can be improved with a generalizable model of similarity. The proposed approach has already been used to build similarity indices in two open-source software tools (CAESAR and VEGA) that make several QSAR models available. In these tools, the similarity index plays a key role for the assessment of the applicability domain.Pubblicat

A rational approach to elucidate human monoamine oxidase molecular selectivity

Designing highly selective human monoamine oxidase (hMAO) inhibitors is a challenging goal on the road to a more effective treatment of depression and anxiety (inhibition of hMAO-A isoform) as well as neurodegenerative diseases (inhibition of hMAO-B isoform). To uncover the molecular rationale of hMAOs selectivity, two recently prepared 2H-chromene-2-ones, namely compounds 1 and 2, were herein chosen as molecular probes being highly selective toward hMAO-A and hMAO-B, respectively. We performed molecular dynamics (MD) studies on four different complexes, cross-simulating one at a time the two hMAO-isoforms (dimer embedded in a lipid bilayer) with the two considered probes. Our comparative analysis on the obtained 100 ns trajectories discloses a stable H-bond interaction between 1 and Gln215 as crucial for ligand selectivity toward hMAO-A whereas a water-mediated interaction might explain the observed hMAO-B selectivity of compound 2. Such hypotheses are further supported by binding free energy calculations carried out applying the molecular mechanics generalized Born surface area (MM-GBSA) method and allowing us to evaluate the contribution of each residue to the observed isoform selectivity. Taken as whole, this study represents the first attempt to explain at molecular level hMAO isoform selectivity and a valuable yardstick for better addressing the design of new and highly selective MAO inhibitors

Repurposing Known Drugs as Covalent and Non-Covalent Inhibitors of the SARS-CoV-2 Papain-like Protease

In the absence of an approved vaccine, developing effective SARS-CoV-2 antivirals is essential to tackle the current pandemic health crisis due to the COVID-19 spread. As any traditional drug discovery program is a time-consuming and costly process requiring more than one decade to be completed, in silico repurposing of existing drugs is the preferred way for rapidly selecting promising clinical candidates. Herein we present a virtual screening campaign to identify covalent and non-covalent inhibitors of the SARS-CoV-2 papain-like protease (PLpro) showing potential multi-target activities for the COVID-19 treatment. A dataset including 688 phase III and 1702 phase IV clinical trial drugs was downloaded from ChEMBL (version 27.1) and docked to the recently released crystal structure of PLpro in complex with a covalently bound peptide inhibitor. The obtained results were analyzed by combining protein-ligand interaction fingerprint similarities, conventional docking scores and MMGBSA binding free energies and allowed the identification of some interesting candidates for further in-vitro testing. To the best of our knowledge, this study represents the first attempt to repurpose drugs for a covalent inhibition of PLpro and could pave the way for new therapeutic strategies against COVID-19.</p

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

Introduction: Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the â\u80\u98big bangâ\u80\u99 of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools

Prediction of Acute Oral Systemic Toxicity Using a Multifingerprint Similarity Approach

The 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

Applicability Domain for QSAR models: where theory meets reality

Quantitative Structure-Activity Relationships are widely acknowledged predictive methods employed, for years, in organic and medicinal chemistry. More recently, they have assumed a central role also in the context of the explorative toxicology for the protection of environment and human health. However, their real-life application has not been always enthusiastically welcomed, being often retrospectively used and, thus, of limited importance for prospective goals. The need of making more trustable predictions has thus addressed studies on the so-called Applicability Domain, which represents the chemical space from which a model is derived and where a prediction is considered to be reliable. In the present study, the authors survey a number of approaches used to build the Applicability Domain. In particular, they will focus on strategies based on: a) physico chemical, b) structural and c) response domains. Moreover, some examples integrating different strategies will be also discussed to meet the needs of both model developers and downstream users

A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

We present MuSSeL, a multifingerprint similarity search algorithm, able to predict putative drug targets for a given query small molecule as well as to return a quantitative assessment of its bioactivity in terms of Ki or IC50 values. Predictions are automatically made exploiting a large collection of high quality experimental bioactivity data available from ChEMBL (version 22.1) combining, in a consensus-like approach, predictions resulting from a similarity search performed using 13 different fingerprint definitions. Importantly, the herein proposed algorithm is also effective in detecting and handling activity cliffs. A calibration set including small molecules present in the last updated version of ChEMBL (version 23) was employed to properly tune the algorithm parameters. Three randomly built external sets were instead challenged for model performances. The potential use of MuSSeL was also challenged by a prospective exercise for the prediction of five bioactive compounds taken from articles published in the Journal of Medicinal Chemistry just few months ago. The paper emphasizes the importance of implementing multifingerprint consensus strategies to increase the confidence in prediction of similarity search algorithms and provides a fast and easy-to-run tool for drug target and bioactivity prediction