Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.

Corrigendum to "Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities" [Comput Struct Biotechnol J vol. 23 (2024) 2141-2151].

[This corrects the article DOI: 10.1016/j.csbj.

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds.

"DompeKeys": a set of novel substructure-based descriptors for efficient chemical space mapping, development and structural interpretation of machine learning models, and indexing of large databases.

The conversion of chemical structures into computer-readable descriptors, able to capture key structural aspects, is of pivotal importance in the field of cheminformatics and computer-aided drug design. Molecular fingerprints represent a widely employed class of descriptors; however, their generation process is time-consuming for large databases and is susceptible to bias. Therefore, descriptors able to accurately detect predefined structural fragments and devoid of lengthy generation procedures would be highly desirable.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database.

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions.

ProfhEX: AI-based platform for small molecules liability profiling.

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates.

Ensemble of structure and ligand-based classification models for hERG liability profiling.

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects.

"Molecular Anatomy": a new multi-dimensional hierarchical scaffold analysis tool.

The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities. In this paper, we present a novel approach called "Molecular Anatomy" as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks.

CATMoS: Collaborative Acute Toxicity Modeling Suite.

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests.

Visualization and Analysis of the REACH-chemical Space with Generative Topographic Mapping.

In the framework of REACH (Registration Evaluation Authorization and restriction of Chemicals) regulation, industries have generated and reported a huge amount of (eco)toxicological data on substance produced or imported in Europe. The registration procedure initiated the creation of a large REACH database of well defined (eco)toxicological properties. Here, the data distribution in the REACH chemical space was analyzed with the help of the Generative Topographic Mapping (GTM) approach.

Accuracy Evaluation of Three Modelling Tools for Occupational Exposure Assessment.

Objectives: The objective of this study is to evaluate the accuracy and robustness of three exposure-modelling tools [STOFFENMANAGER® v.6, European Centre for Ecotoxicology and Toxicology of Chemical Target Risk Assessment v.3.