DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation.

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis.

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.

ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators.

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.

Structural Characterization of the Full-Length Anti-CD20 Antibody Rituximab.

Rituximab, a murine-human chimera, is the first monoclonal antibody (mAb) developed as a therapeutic agent to target CD20 protein. Its Fab domain and its interaction with CD20 have been extensively studied and high-resolution atomic models obtained by X-ray diffraction or cryo-electron microscopy are available. However, the structure of the full-length antibody is still missing as the inherent protein flexibility hampers the formation of well-diffracting crystals and the reconstruction of 3D microscope images. The global structure of rituximab from its dilute solution is here elucidated by small-angle X-ray scattering (SAXS). The limited data resolution achievable by this technique has been compensated by intensive computational modelling that led to develop a new and effective procedure to characterize the average mAb conformation as well as that of the single domains. SAXS data indicated that rituximab adopts an asymmetric average conformation in solution, with a radius of gyration and a maximum linear dimension of 52 Å and 197 Å, respectively. The asymmetry is mainly due to an uneven arrangement of the two Fab units with respect to the central stem (the Fc domain) and reflects in a different conformation of the individual units. As a result, the Fab elbow angle, which is a crucial determinant for antigen recognition and binding, was found to be larger (169°) in the more distant Fab unit than that in the less distant one (143°). The whole flexibility of the antibody has been found to strongly depend on the relative inter-domain orientations, with one of the Fab arms playing a major role. The average structure and the amount of flexibility has been studied in the presence of different buffers and additives, and monitored at increasing temperature, up to the complete unfolding of the antibody. Overall, the structural characterization of rituximab can help in designing next-generation anti-CD20 antibodies and finding more efficient routes for rituximab production at industrial level.

Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach.

We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.

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. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Theoretical insights on acceptor-donor dyads for organic photovoltaics.

The field of organic photovoltaics has witnessed a steady growth in the last few decades and a recent renewal with the blossoming of single-material organic solar cells (SMOSCs). However, due to the intrinsic complexity of these devices (both in terms of their size and of the condensed phases involved), computational approaches to accurately predict their geometrical and electronic structure and to link their microscopic properties to the observed macroscopic behaviour are still lacking. In this work, we have focused on the rationalization of transport dynamics and we have set up a computational approach that makes a combined use of classical simulations and Density Functional Theory with the aim of disclosing the most relevant electronic and structural features of dyads used for SMOSC applications. As a prototype dyad, we have considered a molecule that consists in a dithiafulvalene-functionalized diketopyrrolopyrrole (DPP), acting as an electron donor, covalently linked to a fulleropyrrolidine (Ful), the electron acceptor. Our results, beside a quantitative agreement with experiments, show that the overall observed mobilities result from the competing packing mechanisms of the constituting units within the dyad both in the case of crystalline and amorphous phases. As a consequence, not all stable polymorphs have the same efficiency in transporting holes or electrons which often results in a highly directional carrier transport that is not, in general, a desirable feature for polycrystalline thin-films. The present work, linking microscopic packing to observed transport, thus opens the route for the in silico design of new dyads with enhanced and controlled structural and electronic features.

Enhancing the Sensitivity of Biotinylated Surfaces by Tailoring the Design of the Mixed Self-Assembled Monolayer Synthesis.

Thiolated self-assembled monolayers (SAMs) are typically used to anchor on a gold surface biomolecules serving as recognition elements for biosensor applications. Here, the design and synthesis of N-(2-hydroxyethyl)-3-mercaptopropanamide (NMPA) in biotinylated mixed SAMs is proposed as an alternative strategy with respect to on-site multistep functionalization of SAMs prepared from solutions of commercially available thiols. In this study, the mixed SAM deposited from a 10:1 solution of 3-mercaptopropionic acid (3MPA) and 11-mercaptoundecanoic acid (11MUA) is compared to that resulting from a 10:1 solution of NMPA:11MUA. To this end, surface plasmon resonance (SPR) and attenuated total reflectance infrared (ATR-IR) experiments have been carried out on both mixed SAMs after biotinylation. The study demonstrated how the fine tuning of the SAM features impacts directly on both the biofunctionalization steps, i.e., the biotin anchoring, and the biorecognition properties evaluated upon exposure to streptavidin analyte. Higher affinity for the target analyte with reduced nonspecific binding and lower detection limit has been demonstrated when NMPA is chosen as the more abundant starting thiol. Molecular dynamics simulations complemented the experimental findings providing a molecular rationale behind the performance of the biotinylated mixed SAMs. The present study confirms the importance of the functionalization design for the development of a highly performing biosensor.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Exploring the role of elongation Factor-Like 1 (EFL1) in Shwachman-Diamond syndrome through molecular dynamics.

Shwachman-Diamond Syndrome (SDS) is an autosomal recessive disorder whose patients present mutations in two ribosome assembly proteins, the Shwachman-Bodian-Diamond Syndrome protein (SBDS) and the Elongation Factor-Like 1 (EFL1). Due to the lack of knowledge of the molecular mechanisms responsible for SDS pathogenesis, current therapy is nonspecific and focuses only at alleviating the symptoms. Building on the recent observation that EFL1 single-point mutations clinically manifest as SDS-like phenotype, we carried out comparative Molecular Dynamics (MD) simulations on three mutants, T127A, M882K and R1095Q and wild type EFL1. As supported by small angle X-ray scattering experiments, the obtained data improve the static EFL1 model resulting from the Cryo-electron microscopy and clearly show that all the mutants experience a peculiar rotation, around the hinge region, of domain IV with respect to domains I and II leading to a different conformation respect to that of wild type protein. This study supports the notion that EFL1 function is governed by an allosteric mechanism involving the concerted action of GTPase domain (domain I) and the domain IV and can help point towards new approaches to SDS treatment.Communicated by Ramaswamy H. Sarma.

Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search.

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.

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.

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 (LD50) point prediction, the "nontoxic" (LD50 > 2000 mg/kg) and "very toxic" (LD50<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 LD50 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.

Single-molecule detection with a millimetre-sized transistor.

Label-free single-molecule detection has been achieved so far by funnelling a large number of ligands into a sequence of single-binding events with few recognition elements host on nanometric transducers. Such approaches are inherently unable to sense a cue in a bulk milieu. Conceptualizing cells' ability to sense at the physical limit by means of highly-packed recognition elements, a millimetric sized field-effect-transistor is used to detect a single molecule. To this end, the gate is bio-functionalized with a self-assembled-monolayer of 1012 capturing anti-Immunoglobulin-G and is endowed with a hydrogen-bonding network enabling cooperative interactions. The selective and label-free single molecule IgG detection is strikingly demonstrated in diluted saliva while 15 IgGs are assayed in whole serum. The suggested sensing mechanism, triggered by the affinity binding event, involves a work-function change that is assumed to propagate in the gating-field through the electrostatic hydrogen-bonding network. The proposed immunoassay platform is general and can revolutionize the current approach to protein detection.

Molecular Docking for Predictive Toxicology.

Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology.

Design, Synthesis, and Biological Evaluation of Tetrahydro-ß-carboline Derivatives as Selective Sub-Nanomolar Gelatinase Inhibitors.

Targeting matrix metalloproteinases (MMPs) is a pursued strategy for treating several pathological conditions, such as multiple sclerosis and cancer. Herein, a series of novel tetrahydro-ß-carboline derivatives with outstanding inhibitory activity toward MMPs are present. In particular, compounds 9 f, 9 g, 9 h and 9 i show sub-nanomolar IC50 values. Interestingly, compounds 9 g and 9 i also provide remarkable selectivity toward gelatinases; IC50 =0.15 nm for both toward MMP-2 and IC50 =0.63 and 0.58 nm, respectively, toward MMP-9. Molecular docking simulations, performed by employing quantum mechanics based partial charges, shed light on the rationale behind binding involving specific interactions with key residues of S1' and S3' domains. Taken together, these studies indicate that tetrahydro-ß-carboline represents a promising scaffold for the design of novel inhibitors able to target MMPs and selectively bias gelatinases, over the desirable range of the pharmacokinetics spectrum.

Persulfate Reaction in a Hair-Bleaching Formula: Unveiling the Unconventional Reactivity of 1,13-Diamino-4,7,10-Trioxatridecane.

The stability and unconventional reactivity of 1,13-diamino-4,7,10-trioxatridecane in the presence of NH3, H2O2, and (NH4)2S2O8 are described. The ether-diamine is an ingredient marketed to hair salons and consumers for so-called "plex" services to compensate for hair damage during bleaching. The main reaction product identified is an unexpected azanyl ester derivative. This is considered relevant for the safety evaluation when used in cosmetic products. The mechanism of reaction was explored through DFT calculations. This study represents the first attempt to assess the stability of a plex active in an oxidative environment.

Revealing the Origins of Mechanically Induced Fluorescence Changes in Organic Molecular Crystals.

Mechanofluorochromic molecular materials display a change in fluorescence color through mechanical stress. Complex structure-property relationships in both the crystalline and amorphous phases of these materials govern both the presence and strength of this behavior, which is usually deemed the result of a mechanically induced phase transition. However, the precise nature of the emitting species in each phase is often a matter of speculation, resulting from experimental data that are difficult to interpret, and a lack of an acceptable theoretical model capable of capturing complex environmental effects. With a combined strategy using sophisticated experimental techniques and a new theoretical approach, here the varied mechanofluorochromic behavior of a series of difluoroboron diketonates is shown to be driven by the formation of low-energy exciton traps in the amorphous phase, with a limited number of traps giving rise to the full change in fluorescence color. The results highlight intrinsic structural links between crystalline and amorphous phases, and how these may be exploited for further development of powerful mechanofluorochromic assemblies, in line with modern crystal engineering approaches.