Flexibility in early drug discovery: focus on the beyond-Rule-of-5 chemical space

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.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning with Quantitative Structure-Property Relationship (Tg-QSPR)

The glass transition temperature (Tg) is a crucial characteristic of polyimides (PIs). Developing a Tg predictive model using machine learning methodologies can facilitate the design of PI structures and expedite the development process. In this investigation, a data set comprising 1257 PIs was assembled, with Tg values determined using differential scanning calorimetry. 210 molecular descriptors were computed using RDKit, and subsequently, six distinct feature selection methodologies were employed to refine the descriptor set. Quantitative structure-property relationship models targeting Tg (Tg-QSPR) were then constructed using five ensemble learning algorithms and one deep learning algorithm. These models exhibited high predictive accuracy and robustness, with the CATBoost model demonstrating the highest accuracy, achieving a coefficient of determination of 0.823 for the test set, a mean absolute error of 20.1 °C, and a root-mean-square error of 29.0 °C. The study identified the NumRotatableBonds descriptor as particularly influential on Tg, showing a negative correlation with the property. Additionally, the model's accuracy was validated using ten new PI films not included in the original data set, resulting in absolute errors ranging from 2.5 to 26.9 °C and absolute percentage error rates of 1.0-12.8%. These findings underscore the importance of utilizing extensive and diverse data sets for predictive modeling to enhance accuracy and stability. Furthermore, exploring the interpretability of the model and experimentally validating newly synthesized PIs have augmented the practical utility of the model. Under the guidance of the Tg-QSPR models, it will be possible to accelerate the performance prediction and structural design of PIs in the future.

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often nontransferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, and constitution) and is well-suited for early stage ligand optimization. Using only small data sets, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective ligands. Finally, we constructed the BDL-Cu-2023 data set, composed of 312 bidentate ligands extracted from the Cambridge Structural Database, and screened it with this procedure to identify ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

Recent advances in computational and experimental protein-ligand affinity determination techniques

INTRODUCTION

Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved.

AREAS COVERED

In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design.

EXPERT OPINION

The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.

One chiral fingerprint to find them all

Molecular fingerprints are indispensable tools in cheminformatics. However, stereochemistry is generally not considered, which is problematic for large molecules which are almost all chiral. Herein we report MAP4C, a chiral version of our previously reported fingerprint MAP4, which lists MinHashes computed from character strings containing the SMILES of all pairs of circular substructures up to a diameter of four bonds and the shortest topological distance between their central atoms. MAP4C includes the Cahn-Ingold-Prelog (CIP) annotation (R, S, r or s) whenever the chiral atom is the center of a circular substructure, a question mark for undefined stereocenters, and double bond cis-trans information if specified. MAP4C performs slightly better than the achiral MAP4, ECFP and AP fingerprints in non-stereoselective virtual screening benchmarks. Furthermore, MAP4C distinguishes between stereoisomers in chiral molecules from small molecule drugs to large natural products and peptides comprising thousands of diastereomers, with a degree of distinction smaller than between structural isomers and proportional to the number of chirality changes. Due to its excellent performance across diverse molecular classes and its ability to handle stereochemistry, MAP4C is recommended as a generally applicable chiral molecular fingerprint. SCIENTIFIC CONTRIBUTION: The ability of our chiral fingerprint MAP4C to handle stereoisomers from small molecules to large natural products and peptides is unprecedented and opens the way for cheminformatics to include stereochemistry as an important molecular parameter across all fields of molecular design.

The hapten rigidity improves antibody performances in immunoassay for rifamycins: Immunovalidation and molecular mechanism

The immunogenicity of haptens determines the performance of the resultant antibody for small molecules. Rigidity is one of the basic physicochemical properties of haptens. However, few studies have investigated the effect of hapten rigidity on the strength of an immune response and overall antibody performance. Herein, we introduce three molecular descriptors that quantify hapten rigidity. By using of these descriptors, four rifamycin haptens with varied rigidity were designed. The structural and physicochemical feasibility of the designed haptens was then assessed by computational chemistry. Immunization demonstrated that the strength of induced immune responses, i.e., the titer and affinity of antiserum, was significantly increased with increased rigidity of haptens. Furthermore, molecular dynamic simulations demonstrated conformation constraint of rigid haptens contributed to the initial binding and activation of naïve B cells. Finally, a highly sensitive indirect competitive enzyme-linked immunosorbent assay was developed for detection of rifaximin, with an IC50 of 1.1 μg/L in buffer and a limit of detection of 0.2-11.3 μg/L in raw milk, river water, and soil samples. This work provides new insights into the effect of hapten rigidity on immunogenicity and offers new hapten design strategies for antibody discovery and vaccine development of small molecules.

Uncovering the toxicity mechanisms of a series of carboxylic acids in liver cells through computational and experimental approaches

Hepatotoxicity poses a significant concern in drug design due to the potential liver damage that can be caused by new drugs. Among common manifestations of hepatotoxic damage is lipid accumulation in hepatic tissue, resulting in liver steatosis or phospholipidosis. Carboxylic derivatives are prone to interfere with fatty acid metabolism and cause lipid accumulation in hepatocytes. This study investigates the toxic behaviour of 24 structurally related carboxylic acids in hepatocytes, specifically their ability to cause accumulation of fatty acids and phospholipids. Using high-content screening (HCS) assays, we identified two distinct lipid accumulation patterns. Subsequently, we developed structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models to determine relevant molecular substructures and descriptors contributing to these adverse effects. Additionally, we calculated physicochemical properties associated with lipid accumulation in hepatocytes and examined their correlation with our chemical structure characteristics. To assess the applicability of our findings to a wide range of chemical compounds, we employed two external datasets to evaluate the distribution of our QSAR descriptors. Our study highlights the significance of subtle molecular structural variations in triggering hepatotoxicity, such as the presence of nitrogen or the specific arrangement of substitutions within the carbon chain. By employing our comprehensive approach, we pinpointed specific molecules and elucidated their mechanisms of toxicity, thus offering valuable insights to guide future toxicology investigations.

Discovery of Potent and Selective PI3Kδ Inhibitors for the Treatment of Acute Myeloid Leukemia

PI3Kδ is an essential target correlated to the occurrence and development of acute myeloid leukemia (AML). Herein, we investigated the pyrazolo[3,4-d]pyrimidine derivatives as potent and selective PI3Kδ inhibitors with high therapeutic efficacy toward AML. There were 44 compounds designed and prepared in a four-round optimization, and the biological evaluation showed that (S)-36 exhibited potent PI3Kδ inhibitory activity, high selectivity, and high antiproliferative activities against MV-4-11 and MOLM-13 cells, coupled with high oral bioavailability (F = 59.6%). In the MOLM-13 subcutaneous xenograft model, (S)-36 could significantly suppress the tumor progression with a TGI of 67.81% at an oral administration dosage of 10 mg/kg without exhibiting obvious toxicity. Mechanistically, (S)-36 could robustly inhibit the PI3K/AKT pathway for significant suppression of cell proliferation and remarkable induction of apoptosis both in vitro and in vivo. Thus, compound (S)-36 represents a promising PI3Kδ inhibitor for the treatment of acute myeloid leukemia with high efficacy.

The search for novel proline analogs for viscosity reduction and stabilization of highly concentrated monoclonal antibody solutions

Administration of monoclonal antibodies (mAbs) is currently focused on subcutaneous injection associated with increased patient adherence and reduced treatment cost, leading to sustainable healthcare. The main bottleneck is low volume that can be injected, requiring highly concentrated mAb solutions. The latter results in increased solution viscosity with pronounced mAb aggregation propensity because of intensive protein-protein interactions. Small molecule excipients have been proposed to restrict the protein-protein interactions, contributing to reduced viscosity. The aim of the study was to discover novel compounds that reduce the viscosity of highly concentrated mAb solution. First, the chemical space of proline analogs was explored and 35 compounds were determined. Viscosity measurements revealed that 18 proline analogs reduced the mAb solution viscosity similar to or more than proline. The compounds forming both electrostatic and hydrophobic interactions with mAb reduced the viscosity of the formulation more efficiently without detrimentally effecting mAb physical stability. A correlation between the level of interaction and viscosity-reducing effect was confirmed with molecular dynamic simulations. Structure rigidity of the compounds and aromaticity contributed to their viscosity-reducing effect, dependent on molecule size. The study results highlight the novel proline analogs as an effective approach in viscosity reduction in development of biopharmaceuticals for subcutaneous administration.

Exploring the chemical space of orally bioavailable PROTACs

A principal challenge in the discovery of proteolysis targeting chimeras (PROTACs) as oral medications is their bioavailability. To facilitate drug design, it is therefore essential to identify the chemical space where orally bioavailable PROTACs are more likely to be situated. To this aim, we extracted structure-bioavailability insights from published data using traditional 2D descriptors, thereby shedding light on their potential and limitations as drug design tools. Subsequently, we describe cutting-edge experimental, computational and hybrid design strategies based on 3D descriptors, which show promise for enhancing the probability of discovering PROTACs with high oral bioavailability.

1,2,3-Triazole Hybrids Containing Isatins and Phenolic Moieties: Regioselective Synthesis and Molecular Docking Studies

The synthesis of hybrid molecules is one of the current strategies of drug discovery for the development of new lead compounds. The 1,2,3-triazole moiety represents an important building block in Medicinal Chemistry, extensively present in recent years. In this paper, we presented the design and the synthesis of new 1,2,3-triazole hybrids, containing both an isatine and a phenolic core. Firstly, the non-commercial azide and the alkyne synthons were prepared by different isatines and phenolic acids, respectively. Then, the highly regioselective synthesis of 1,4-disubstituted triazoles was obtained in excellent yields by a click chemistry approach, catalyzed by Cu(I). Finally, a molecular docking study was performed on the hybrid library, finding four different therapeutic targets. Among them, the most promising results were obtained on 5-lipoxygenase, an enzyme involved in the inflammatory processes.

Synthesis and structure-activity relationship of novel thiazole aminoguanidines against MRSA and Escherichia coli

Novel lead thiazole aminoguanidines exhibited strong activity against Gram-positive bacteria. The potential targets of these substances are undecaprenyl diphosphate synthase (UPPS) and undecaprenyl diphosphate phosphatase (UPPP). Here, we report the synthesis and antibacterial evaluation of a library of thiazole aminoguanidines analogues, wherein the rotatable bond is inserted between the C2 position of thiazole and hydrophobic group. The molecular flexibility is increased, and new analogues with strong activity against MRSA and E. coli are produced. The best compound 4i showed rapid sterilization and low tendency to induce bacterial resistance. The IC50 of compound 4i to EcUPPS enzyme is 145 μmol L-1 (58 μg mL-1). Compound 4i can also inhibit and destroy bacterial biofilms. These thiazole aminoguanidines can be developed as potential therapeutic candidates in the future.

Identifying Biomarkers and Therapeutic Targets by Multiomic Analysis for HNSCC: Precision Medicine and Healthcare Management

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the major types of cancer, with 900,000 cases and over 400,000 deaths annually. It constitutes 3-4% of all cancers in Europe and western countries. As early diagnosis is the key to treating the disease, reliable biomarkers play an important role in the precision medicine of HNSCC. Despite treatments, the survival rate of cancer patients remains unchanged, and this is mainly due to the failure to detect the disease early. Thus, the objective of this study is to identify reliable biomarkers for head and neck cancers for better healthcare management. Methods: In this study, all available, curated human genes were screened for their expression against HNSCC TCGA patient samples using genomic and proteomic data by various bioinformatic approaches and datamining. Docking studies were performed using AutoDock or online virtual screening tools for identifying potential ligands. Results: Sixty genes were short-listed, and most of them show a consistently higher expression in head and neck patient samples at both the mRNA and the protein level. Irrespective of human papillomavirus (HPV) status, all of them show a higher expression in cancer samples. The higher expression of 30 genes shows adverse effects on patient survival. Out of the 60 genes, 12 genes have crystal structures and druggable potential. We show that genes such as GTF2H4, HAUS7, MSN, and MNDA could be targets of Pembrolizumab and Nivolumab, which are approved monoclonal antibodies for HNSCC. Conclusion: Sixty genes are identified as potential biomarkers for head and neck cancers based on their consistent and statistically significantly higher expression in patient samples. Four proteins have been identified as potential drug targets based on their crystal structure. However, the utility of these candidate genes has to be further tested using patient samples.

Synthesis and Biophysical Characterization of Fingolimod Derivatives as Cardiac Troponin Antagonists

Calcium binding to cardiac troponin C (cTnC) in the thin filaments acts as a trigger for cardiac muscle contraction. The N-lobe of cTnC (NcTnC) undergoes a conformational change in the presence of calcium that allows for interaction with the switch region of cardiac troponin I (cTnISP), releasing its inhibitory effect on the thin filament structure. The small molecule fingolimod inhibits cTnC-cTnISP interactions via electrostatic repulsion between its positively charged tail and positively charged residues in cTnISP and acts as a calcium desensitizer of the contractile myofilaments. Here we investigate the structure-activity relationship of the fingolimod hydrophobic headgroup and show that increasing the alkyl chain length increases both its affinity for NcTnC and its inhibitory effect on the NcTnC-cTnISP interaction and that decreasing flexibility completely abolishes these effects. Strikingly, the longer derivatives have no effect on the calcium affinity of cTnC, suggesting that they act as better inhibitors.

Molecular chameleons in drug discovery

Molecular chameleons possess a flexibility that allows them to dynamically shield or expose polar functionalities in response to the properties of the environment. Although the concept of molecular chameleons was introduced already in 1970, interest in them has grown considerably since the 2010s, when drug discovery has focused to an increased extent on new chemical modalities. Such modalities include cyclic peptides, macrocycles and proteolysis-targeting chimeras, all of which reside in a chemical space far from that of traditional small-molecule drugs. Both cell permeability and aqueous solubility are required for the oral absorption of drugs. Engineering these properties, and potent target binding, into the larger new modalities is a more daunting task than for traditional small-molecule drugs. The ability of chameleons to adapt to different environments may be essential for success. In this Review, we provide both general and theoretical insights into the realm of molecular chameleons. We discuss why chameleons have come into fashion and provide a do-it-yourself toolbox for their design; we then provide a glimpse of how advanced in silico methods can support molecular chameleon design.

Addressing Challenges of Macrocyclic Conformational Sampling in Polar and Apolar Solvents: Lessons for Chameleonicity

We evaluated a workflow to reliably sample the conformational space of a set of 47 peptidic macrocycles. Starting from SMILES strings, we use accelerated molecular dynamics simulations to overcome high energy barriers, in particular, the cis-trans isomerization of peptide bonds. We find that our approach performs very well in polar solvents like water and dimethyl sulfoxide. Interestingly, the protonation state of a secondary amine in the ring only slightly influences the conformational ensembles of our test systems. For several of the macrocycles, determining the conformational distribution in chloroform turns out to be considerably more challenging. Especially, the choice of partial charges crucially influences the ensembles in chloroform. We address these challenges by modifying initial structures and the choice of partial charges. Our results suggest that special care has to be taken to understand the configurational distribution in apolar solvents, which is a key step toward a reliable prediction of membrane permeation of macrocycles and their chameleonic properties.

Bioinformatics Tools for the Analysis of Active Compounds Identified in Ranunculaceae Species

The chemical compounds from extracts of three Ranunculaceae species, Aconitum toxicum Rchb., Anemone nemorosa L. and Helleborus odorus Waldst. & Kit. ex Willd., respectively, were isolated using the HPLC purification technique and analyzed from a bioinformatics point of view. The classes of compounds identified based on the proportion in the rhizomes/leaves/flowers used for microwave-assisted extraction and ultrasound-assisted extraction were alkaloids and phenols. Here, the quantifying of pharmacokinetics, pharmacogenomics and pharmacodynamics helps us to identify the actual biologically active compounds. Our results showed that (i) pharmacokinetically, the compounds show good absorption at the intestinal level and high permeability at the level of the central nervous system for alkaloids; (ii) regarding pharmacogenomics, alkaloids can influence tumor sensitivity and the effectiveness of some treatments; (iii) and pharmacodynamically, the compounds of these Ranunculaceae species bind to carbonic anhydrase and aldose reductase. The results obtained showed a high affinity of the compounds in the binding solution at the level of carbonic anhydrases. Carbonic anhydrase inhibitors extracted from natural sources can represent the path to new drugs useful both in the treatment of glaucoma, but also of some renal, neurological and even neoplastic diseases. The identification of natural compounds with the role of inhibitors can have a role in different types of pathologies, both associated with studied and known receptors such as carbonic anhydrase and aldose reductase, as well as new pathologies not yet addressed.

Advancing Strategies for Proteolysis-Targeting Chimera Design

Proteolysis-targeting chimeras (PROTACs) have shown great therapeutic potential by degrading various disease-causing proteins, particularly those related to tumors. Therefore, the introduction of PROTACs has ushered in a new chapter of antitumor drug development, marked by significant advances over recent years. Herein, we describe recent developments in PROTAC technology, focusing on design strategy, development workflow, and future outlooks. We also discuss potential opportunities and challenges for PROTAC research.

Going Viral: An Investigation into the Chameleonic Behaviour of Antiviral Compounds

The ability to adjust conformations in response to the polarity of the environment, i.e. molecular chameleonicity, is considered to be important for conferring both high aqueous solubility and high cell permeability to drugs in chemical space beyond Lipinski's rule of 5. We determined the conformational ensembles populated by the antiviral drugs asunaprevir, simeprevir, atazanavir and daclatasvir in polar (DMSO-d₆ ) and non-polar (chloroform) environments with NMR spectroscopy. Daclatasvir was fairly rigid, whereas the first three showed large flexibility in both environments, that translated into major differences in solvent accessible 3D polar surface area within each conformational ensemble. No significant differences in size and polar surface area were observed between the DMSO-d₆ and chloroform ensembles of these three drugs. We propose that such flexible compounds are characterized as "partial molecular chameleons" and hypothesize that their ability to adopt conformations with low polar surface area contributes to their membrane permeability and oral absorption.

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure-activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch-bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.

Structural diversity-guided optimization of carbazole derivatives as potential cytotoxic agents

Carbazole alkaloids, as an important class of natural products, have been widely reported to have extensive biological activities. Based on our previous three-component reaction to construct carbazole scaffolds, we introduced a methylene group to provide a rotatable bond, and designed series of carbazole derivatives with structural diversity including carbazole amide, carbazole hydrazide and carbazole hydrazone. All synthesized carbazole derivatives were evaluated for their in vitro cytotoxic activity against 7901 (gastric adenocarcinoma), A875 (human melanoma) and MARC145 (African green monkey kidney) cell lines. The preliminary results indicated that compound 14a exhibited high inhibitory activities on 7901 and A875 cancer cells with the lowest IC₅₀ of 11.8 ± 1.26 and 9.77 ± 8.32 μM, respectively, which might be the new lead compound for discovery of novel carbazole-type anticancer agents.

Conformational Sampling Deciphers the Chameleonic Properties of a VHL-Based Degrader

Chameleonicity (the capacity of a molecule to adapt its conformations to the environment) may help to identify orally bioavailable drugs in the beyond-Rule-of-5 chemical space. Computational methods to predict the chameleonic behaviour of degraders have not yet been reported and the identification of molecular chameleons still relies on experimental evidence. Therefore, there is a need to tune predictions with experimental data. Here, we employ PROTAC-1 (a passively cell-permeable degrader), for which NMR and physicochemical data prove the chameleonic behaviour, to benchmark the capacity of two conformational sampling algorithms and selection schemes. To characterize the conformational ensembles in both polar and nonpolar environments, we compute three molecular properties proven to be essential for cell permeability: conformer shape (radius of gyration), polarity (3D PSA), and the number of intramolecular hydrogen bonds. Energetic criteria were also considered. Infographics monitored the simultaneous variation of those properties in computed and NMR conformers. Overall, we provide key points for tuning conformational sampling tools to reproduce PROTAC-1 chameleonicity according to NMR evidence. This study is expected to improve the design of PROTAC drugs and the development of computational sustainable strategies to exploit the potential of new modalities in drug discovery.

In silico package models for deriving values of solute parameters in linear solvation energy relationships

Environmental partitioning influences fate, exposure and ecological risks of chemicals. Linear solvation energy relationship (LSER) models may serve as efficient tools for estimating environmental partitioning parameter values that are commonly deficient for many chemicals. Nonetheless, scarcities of empirical solute parameter values of LSER models restricted the application. This study developed and evaluated in silico methods and models to derive the values, in which excess molar refraction, molar volume and logarithm of hexadecane/air partition coefficient were computed from density functional theory; dipolarity/polarizability parameter, solute H-bond acidity and basicity parameters were predicted by quantitative structure-activity relationship models developed with theoretical molecular descriptors. New LSER models on four physicochemical properties relevant with environmental partitioning (n-octanol/water partition coefficients, n-octanol/air partition coefficients, water solubilities, sub-cooled liquid vapour pressures) were constructed using the in silico solute parameter values, which exhibited comparable performance with conventional LSER models using the empirical solute parameter values. The package models for deriving the LSER solute parameter values, with advantages that they are free of instrumental determinations, may lay the foundation for high-throughput estimating environmental partition parameter values of diverse organic chemicals.

Comparative analysis of an anthraquinone and chalcone derivatives-based virtual combinatorial library. A cheminformatics "proof-of-concept" study

A Laplacian scoring algorithm for gene selection and the Gini coefficient to identify the genes whose expression varied least across a large set of samples were the state-of-the-art methods used here. These methods have not been trialed for their feasibility in cheminformatics. This was a maiden attempt to investigate a complete comparative analysis of an anthraquinone and chalcone derivatives-based virtual combinatorial library. This computational "proof-of-concept" study illustrated the combinatorial approach used to explain how the structure of the selected natural products (NPs) undergoes molecular diversity analysis. A virtual combinatorial library (1.6 M) based on 20 anthraquinones and 24 chalcones was enumerated. The resulting compounds were optimized to the near drug-likeness properties, and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and NPs from ZINC 15. UMAP and PCA were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score and Gini coefficient were applied to delineate feature selection and selectivity among properties, respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko's and the central scaffolds systems, calculating three fingerprint descriptors and analyzing their diversity by PCA and SOM. The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of the 1.6 M database was more prominent than with the NPs dataset. A Laplacian score prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923), respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81-0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs and 1.6 M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment.

Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Refinement of Computational Access to Molecular Physicochemical Properties: From Ro5 to bRo5

There is a need of computational tools to rank bRo5 drug candidates in the very early phases of drug discovery when chemical matter is unavailable. In this study, we selected three compounds: (a) a Ro5 drug (Pomalidomide), (b) a bRo5 orally available drug (Saquinavir), and (c) a polar PROTAC (CMP 98) to focus on computational access to physicochemical properties. To provide a benchmark, the three compounds were first experimentally characterized for their lipophilicity, polarity, IMHBs, and chameleonicity. To reproduce the experimental information content, we generated conformer ensembles with conformational sampling and molecular dynamics in both water and nonpolar solvents. Then we calculated Rgyr, 3D PSA, and IMHB number. An innovative pool of strategies for data analysis was then provided. Overall, we report a contribution to close the gap between experimental and computational methods for characterizing bRo5 physicochemical properties.

Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor

With the promotion of carbon neutrality, it is also important to synchronously promote the assessment and sustainable management of chemicals so as to protect public health. Humans and animals are possibly exposed to endocrine disruptors that have inhibitory effects on thyroid stimulating hormone receptor (TSHR). As such, it is important to identify chemicals that inhibit TSHR and to develop models to predict their inhibitory activity. In this study, 5952 compounds derived from a cyclic adenosine monophosphate (cAMP) analysis, a key signaling pathway in thyrocytes, were used to establish a binary classification model comparing methods that included random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR). The prediction model based on RF showed the highest identification accuracy for revealing chemicals that may inhibit TSHR. For the RF model, recall was calculated at 0.89, balance accuracy was 0.85, and its receiver operating characteristic (ROC) curve-area under (AUC) was 0.92, indicating that the model had very high predictive capacity. The lowest CDocker energy (CE) and CDocker interaction energy (CIE) for chemicals and TSHR were determined and were subsequently introduced into the predictive model as descriptors. A regression model, extreme gradient boosting-Regression (XGBR), was successfully established yielding an R² = 0.65 to predict inhibitory activity for active compounds. Parameters that included dissociation characteristics, molecular structure, and binding energy were all key factors in the predictive model. We demonstrate that QSAR models are useful approaches, not only for identifying chemicals that inhibit TSHR, but for predicting inhibitory activity of active compounds.

New N-Alkylated Heterocyclic Compounds as Prospective NDM1 Inhibitors: Investigation of In Vitro and In Silico Properties

A new family of pyrazole-based compounds (1–15) was synthesized and characterized using different physicochemical analyses, such as FTIR, UV-Visible, ¹H, ¹³C NMR, and ESI/LC-MS. The compounds were evaluated for their in vitro antifungal and antibacterial activities against several fungal and bacterial strains. The results indicate that some compounds showed excellent antibacterial activity against E. coli, S. aureus, C. freundii, and L. monocytogenes strains. In contrast, none of the compounds had antifungal activity. Molecular electrostatic potential (MEP) map analyses and inductive and mesomeric effect studies were performed to study the relationship between the chemical structure of our compounds and the biological activity. In addition, molecular docking and virtual screening studies were carried out to rationalize the antibacterial findings to characterize the modes of binding of the most active compounds to the active pockets of NDM1 proteins.

Predicting Solubility of Newly-Approved Drugs (2016–2020) with a Simple ABSOLV and GSE(Flexible-Acceptor) Consensus Model Outperforming Random Forest Regression

This study applies the ‘Flexible-Acceptor’ variant of the General Solubility Equation, GSE(Φ,B), to the prediction of the aqueous intrinsic solubility, log₁₀S₀, of FDA recently-approved (2016–2020) ‘small-molecule’ new molecular entities (NMEs). The novel equation had been shown to predict the solubility of drugs beyond Lipinski’s ‘Rule of 5’ chemical space (bRo5) to a precision nearly matching that of the Random Forest Regression (RFR) machine learning method. Since then, it was found that the GSE(Φ,B) appears to work well not only for bRo5 NMEs, but also for Ro5 drugs. To put context to GSE(Φ,B), Yalkowsky’s GSE(classic), Abraham’s ABSOLV, and Breiman’s RFR models were also applied to predict log₁₀ S₀ of 72 newly-approve NMEs, for which useable reported solubility values could be accessed (nearly 60% from FDA New Drug Application published reports). Except for GSE (classic), the prediction models were retrained with an enlarged version of the Wiki-pS₀ database (nearly 400 added log₁₀ S₀ entries since our recent previous study). Thus, these four models were further validated by the additional independent solubility measurements which the newly-approved drugs introduced. The prediction methods ranked RFR ~ GSE (Φ,B) > ABSOLV > GSE (classic) in performance. It was further demonstrated that the biases generated in the four separate models could be nearly eliminated in a consensus model based on the average of just two of the methods: GSE (Φ,B) and ABSOLV. The resulting consensus prediction equation is simple in form and can be easily incorporated into spreadsheet calculations. Even more significant, it slightly outperformed the RFR method.

Cell Permeability of Isomeric Macrocycles: Predictions and NMR Studies

Conformation-dependent 3D descriptors have been shown to provide better predictions of the physicochemical properties of macrocycles than 2D descriptors. However, the computational identification of relevant conformations for macrocycles is nontrivial. Herein, we report that the Caco-2 cell permeability difference between a pair of diastereomeric macrocycles correlated with their solvent accessible 3D polar surface area and radius of gyration. The descriptors were calculated from the macrocycles’ solution-phase conformational ensembles and independently from ensembles obtained by conformational sampling. Calculation of the two descriptors for three other stereo- and regioisomeric macrocycles also allowed the correct ranking of their cell permeability. Methods for conformational sampling may thus allow ranking of passive permeability for moderately flexible macrocycles, thereby contributing to the prioritization of macrocycles for synthesis in lead optimization.

Permeability prediction in the beyond-Rule-of 5 chemical space: Focus on cyclic hexapeptides

Cyclic peptides (CPs) are gaining more and more relevance in drug discovery. Since one of their main drawbacks is poor permeability, the discovery of new orally available CP drugs requires computational tools that predict CP permeability in very early drug discovery. In this study we used a literature dataset of 62 cyclic hexapeptides to evaluate the performances of a number of in silico tools based on different computational theory to model and rationalize PAMPA and Caco-2 permeability values. In particular, we submitted the dataset to a) online calculators, b) QSPR strategies, c) a physics-based tool, d) a mixed approach and e) a kinetic method. This latter is an emergent strategy in which a few relevant conformations retrieved from a set of molecular dynamics (MD) simulations by the Markov State Model (MSM) are used to establish the compounds permeability. Both free and commercial software were used. Results were compared with a model based on experimental physicochemical descriptors. All the computational approaches but online calculators performed quite well and show that lipophilicity and not polarity is the main determinant of the investigated event. A second major outcome of the study is that the impact of flexibility on the permeability of the considered dataset cannot be unambiguously assessed. Finally, our comparative analysis, which also included not common applied strategies, allowed a sound evaluation of the pros and cons of the applied computational approaches.

Defining and navigating macrocycle chemical space

Macrocyclic compounds (MCs) are of growing interest for inhibition of challenging drug targets. We consider afresh what structural and physicochemical features could be relevant to the bioactivity of this compound class. Using these features, we performed Principal Component Analysis to map oral and non-oral macrocycle drugs and clinical candidates, and also commercially available synthetic MCs, in structure–property space. We find that oral MC drugs occupy defined regions that are distinct from those of the non-oral MC drugs. None of the oral MC regions are effectively sampled by the synthetic MCs. We identify 13 properties that can be used to design synthetic MCs that sample regions overlapping with oral MC drugs. The results advance our understanding of what molecular features are associated with bioactive and orally bioavailable MCs, and illustrate an approach by which synthetic chemists can better evaluate MC designs. We also identify underexplored regions of macrocycle chemical space.

Macrocyclic compounds (MCs) are of high interest for inhibition of challenging drug targets, but existing oral MC drugs occupy regions of chemical space that are not well sampled by many available synthetic MC chemotypes.

Do Drug-likeness Rules Apply to Oral Prodrugs?

This paper describes a comparative analysis of the physicochemical and structural properties of prodrugs and their corresponding drugs with regard to drug-likeness rules. The dataset used in this work was obtained from the DrugBank. Sixty-five pairs of prodrugs/drugs were retrieved and divided into the following categories: carrier-linked to increase hydrophilic character, carrier-linked to increase absorption, and bioprecursors. We compared the physicochemical properties related to drug-likeness between prodrugs and drugs. Our results show that prodrugs do not always follow Lipinski's Rule of 5, especially as we observed 15 prodrugs with more than 10 hydrogen bond acceptors and 18 with a molecular weight greater than 500 Da. This fact highlights the importance of extending Lipinski's rules to encompass other parameters as both strategies (filtering of drug-like chemical libraries and prodrug design) aim to improve the bioavailability of compounds. Therefore, critical reasoning is fundamental to determine whether a structure has drug-like properties or could be considered a potential orally active compound in the drug-design pipeline.

PROTACs and Building Blocks: The 2D Chemical Space in Very Early Drug Discovery

Targeted protein degradation by PROTACs has emerged as a new modality for the knockdown of a range of proteins, and, more recently, it has become increasingly clear that the PROTAC chemical space requires characterization through a pool of ad hoc physicochemical descriptors. In this study, a new database named PROTAC-DB that provides extensive information about PROTACs and building blocks was used to obtain the 2D chemical structures of about 1600 PROTACs, 60 E3 ligands, 800 linkers, and 202 warheads. For every structure, we calculated a pool of seven 2D descriptors carefully identified as informative for large and flexible structures. For comparison purposes, the same procedure was applied to a dataset of about 50 bRo5 approved drugs reported in the literature. Correlation matrices, PCAs, box plots, and other graphical tools were used to define and understand the chemical space covered by PROTACs and building blocks in relation to other compounds. Results show that linkers have different properties than E3 ligands and warheads. Polar descriptors additivity is not respected when passing from building blocks to degraders. Moreover, a very preliminary analysis based on three PROTACs with high, intermediate, and low permeability showed how the most permeable compounds seem to occupy a region closer to bRo5 drugs and, thus, exhibit different properties than impermeable compounds. Finally, a second database, PROTACpedia, was used to discuss the relevance of physicochemical descriptors on degradation activity.

Steering New Drug Discovery Campaigns: Permeability, Solubility, and Physicochemical Properties in the bRo5 Chemical Space

An increasing number of drug discovery programs concern compounds in the beyond rule of 5 (bRo5) chemical space, such as cyclic peptides, macrocycles, and degraders. Recent results show that common paradigms of property-based drug design need revision to be applied to larger and more flexible compounds. A virtual event entitled "Solubility, permeability and physico-chemical properties in the bRo5 chemical space" was organized to provide preliminary guidance on how to make the discovery of oral drugs in the bRo5 space more effective. The four speakers emphasized the importance of the bRo5 space as a source of new oral drugs and provided examples of experimental and computational methods specifically tailored for design and optimization in this chemical space.

Predicting the Permeability of Macrocycles from Conformational Sampling - Limitations of Molecular Flexibility

Macrocycles constitute superior ligands for targets that have flat binding sites but often require long synthetic routes, emphasizing the need for property prediction prior to synthesis. We have investigated the scope and limitations of machine learning classification models and of regression models for predicting the cell permeability of a set of denovo-designed, drug-like macrocycles. 2D-Based classification models, which are fast to calculate, discriminated between macrocycles that had low-medium and high permeability and may be used as virtual filters in early drug discovery projects. Importantly, stereo- and regioisomer were correctly classified. QSPR studies of two small sets of comparator drugs suggested that use of 3D descriptors, calculated from biologically relevant conformations, would allow development of more precise regression models for late phase drug projects. However, a 3D permeability model could only be developed for a rigid series of macrocycles. Comparison of NMR based conformational analysis with in silico conformational sampling indicated that this shortcoming originates from the inability of the molecular mechanics force field to identify the relevant conformations for flexible macrocycles. We speculate that a Kier flexibility index of ≤10 constitutes a current upper limit for reasonably accurate 3D prediction of macrocycle cell permeability.

"Flexible-Acceptor" General Solubility Equation for beyond Rule of 5 Drugs

This study describes a novel nonlinear variant of the well-known Yalkowsky general solubility equation (GSE). The modified equation can be trained with small molecules, mostly from the Lipinski Rule of 5 (Ro5) chemical space, to predict the intrinsic aqueous solubility, S₀, of large molecules (MW > 800 Da) from beyond the rule of 5 (bRo5) space, to an accuracy almost equal to that of a recently described random forest regression (RFR) machine learning analysis. The new approach replaces the GSE constant factors in the intercept (0.5), the octanol-water log P (-1.0), and melting point, mp (-0.01) terms with simple exponential functions incorporating the sum descriptor, Φ+B (Kier Φ molecular flexibility and Abraham H-bond acceptor potential). The constants in the modified three-variable (log P, mp, Φ+B) equation were determined by partial least-squares (PLS) refinement using a small-molecule log S₀ training set (n = 6541) of mostly druglike molecules. In this "flexible-acceptor" GSE(Φ,B) model, the coefficient of log P (normally fixed at -1.0) varies smoothly from -1.1 for rigid nonionizable molecules (Φ+B = 0) to -0.39 for typically flexible (Φ ∼ 20, B ∼ 6) large molecules. The intercept (traditionally fixed at +0.5) varies smoothly from +1.9 for completely inflexible small molecules to -2.2 for typically flexible large molecules. The mp coefficient (-0.007) remains practically constant, near the traditional value (-0.01) for most molecules, which suggests that the small-to-large molecule continuum is mainly solvation responsive, apparently with only minor changes in the crystal lattice contributions. For a test set of 32 large molecules (e.g., cyclosporine A, gramicidin A, leuprolide, nafarelin, oxytocin, vancomycin, and mostly natural-product-derived therapeutics used in infectious/viral diseases, in immunosuppression, and in oncology) the modified equation predicted the intrinsic solubility with a root-mean-square error of 1.10 log unit, compared to 3.0 by the traditional GSE, and 1.07 by RFR.

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome

BACKGROUND

Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules.

RESULTS

Here we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints.

CONCLUSION

MAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.