Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts

Dipeptide-Derived Alkynes as Potent and Selective Irreversible Inhibitors of Cysteine Cathepsins

The potential of designing irreversible alkyne-based inhibitors of cysteine cathepsins by isoelectronic replacement in reversibly acting potent peptide nitriles was explored. The synthesis of the dipeptide alkynes was developed with special emphasis on stereochemically homogeneous products obtained in the Gilbert-Seyferth homologation for C≡C bond formation. Twenty-three dipeptide alkynes and 12 analogous nitriles were synthesized and investigated for their inhibition of cathepsins B, L, S, and K. Numerous combinations of residues at positions P1 and P2 as well as terminal acyl groups allowed for the derivation of extensive structure-activity relationships, which were rationalized by computational covalent docking for selected examples. The determined inactivation constants of the alkynes at the target enzymes span a range of >3 orders of magnitude (3-10 133 M^-1 s^-1). Notably, the selectivity profiles of alkynes do not necessarily reflect those of the nitriles. Inhibitory activity at the cellular level was demonstrated for selected compounds.

Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules

Ever since coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic on March 11, 2020, by the WHO, a concerted effort has been made to find compounds capable of acting on the virus and preventing its replication. In this context, researchers have refocused part of their attention on certain natural compounds that have shown promising effects on the virus. Considering the importance of this topic in the current context, this study aimed to present a critical review and analysis of the main reports of plant-derived compounds as possible inhibitors of the two SARS-CoV-2 proteases: main protease (Mpro) and Papain-like protease (PLpro). From the search in the PubMed database, a total of 165 published articles were found that met the search patterns. A total of 590 unique molecules were identified from a total of 122 articles as potential protease inhibitors. At the same time, 114 molecules reported as natural products and with annotation of theoretical support and antiviral effects were extracted from the COVID-19 Help database. After combining the molecules extracted from articles and those obtained from the database, we identified 648 unique molecules predicted as potential inhibitors of Mpro and/or PLpro. According to our results, several of the predicted compounds with higher theoretical confidence are present in many plants used in traditional medicine and even food, such as flavonoids, carboxylic acids, phenolic acids, triterpenes, terpenes phytosterols, and triterpenoids. These are potential inhibitors of Mpro and PLpro. Although the predictions of several molecules against SARS-CoV-2 are promising, little experimental information was found regarding certain families of compounds. Only 45 out of the 648 unique molecules have experimental data validating them as inhibitors of Mpro or PLpro, with the most frequent scaffold present in these 45 compounds being the flavone. The novelty of this work lies in the analysis of the structural diversity of the chemical space among the molecules predicted as inhibitors of SARS-CoV-2 Mpro and PLpro proteases and the comparison to those molecules experimentally validated. This work emphasizes the need for experimental validation of certain families of compounds, preferentially combining classical enzymatic assays with interaction-based methods. Furthermore, we recommend checking the presence of Pan-Assay Interference Compounds (PAINS) and the presence of molecules previously reported as inhibitors of Mpro or PLpro to optimize resources and time in the discovery of new SARS-CoV-2 antivirals from plant-derived molecules.

Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design

Matched molecular pairs (MMPs) are nowadays a commonly applied concept in drug design. They are used in many computational tools for structure-activity relationship analysis, biological activity prediction, or optimization of physicochemical properties. However, until now it has not been shown in a rigorous way that MMPs, that is, changing only one substituent between two molecules, can be predicted with higher accuracy and precision in contrast to any other chemical compound pair. It is expected that any model should be able to predict such a defined change with high accuracy and reasonable precision. In this study, we examine the predictability of four classical properties relevant for drug design ranging from simple physicochemical parameters (log D and solubility) to more complex cell-based ones (permeability and clearance), using different data sets and machine learning algorithms. Our study confirms that additive data are the easiest to predict, which highlights the importance of recognition of nonadditivity events and the challenging complexity of predicting properties in case of scaffold hopping. Despite deep learning being well suited to model nonlinear events, these methods do not seem to be an exception of this observation. Though they are in general performing better than classical machine learning methods, this leaves the field with a still standing challenge.

Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces

The distributions of physicochemical property values, like the octanol-water partition coefficient, are routinely calculated to describe and compare virtual chemical libraries. Traditionally, these distributions are derived by processing each member of a library individually and summarizing all values in a distribution. This process becomes impractical when operating on chemical spaces which surpass billions of compounds in size. In this work, we present a novel algorithmic method called SpaceProp for the property distribution calculation of large nonenumerable combinatorial fragment spaces. The novel method follows a combinatorial approach and is able to calculate physicochemical property distributions of prominent spaces like Enamine's REAL Space, WuXi's GalaXi Space, and OTAVA's CHEMriya Space for the first time. Furthermore, we present a first approach of optimizing property distributions directly in combinatorial fragment spaces.

Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging

In early-stage drug discovery, the hit-to-lead optimization (or "hit expansion") stage entails starting from a newly identified active compound and improving its potency or other properties. Traditionally, this process relies on synthesizing and evaluating a series of analogues to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogues with improved potency. Our protocol begins from an inhibitor of the target kinase and generalizes the synthetic route used to access it. By searching for commercially available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions─or billions─of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into single new compounds and which are not. Further, the use of pairwise approximation allows interaction energies to be assigned to each compound in the library without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFR^T790M, and ACK1. In each example, the enumerated library includes additional analogues reported by the original study to have activity, and these analogues are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.

Cytotoxic and Antifungal Amides Derived from Ferulic Acid: Molecular Docking and Mechanism of Action

Amides derived from ferulic acid have a wide spectrum of pharmacological activities, including antitumor and antifungal activity. In the present study, a series of ten amides were obtained by coupling reactions using the reagents (benzotriazol-1-yloxy) tripyrrolidinophosphonium hexafluorophosphate (PyBOP) and N,N′-dicyclohexylcarbodiimide (DCC). All the compounds were identified on the basis of their IR, ¹H- and ¹³C-NMR, HRMS data, and with yields ranging from 43.17% to 91.37%. The compounds were subjected to cytotoxic tests by the alamar blue technique and antifungal screening by the broth microdilution method to determine the minimum inhibitory concentration (MIC). The amides 10 and 11 displayed the best result in both biological evaluations, and compound 10 was the most potent and selective in HL-60 cancer cells, with no cytotoxicity on healthy cells. This amide had antifungal activity in all strains and had the lowest MIC against Candida albicans and Candida tropicalis. The possible mechanism of antifungal action occurs via the fungal cell wall. Molecular modeling suggested that compounds 10 and 11 interact with the enzymes GWT1 and GSC1, which are essential for the development of C. albicans. The findings of the present study demonstrated that compounds 10 and 11 may be used as a platform in drug development in the future.

Nonadditivity in public and inhouse data: implications for drug design

Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity and additivity of substituent contributions. These techniques are challenged by nonadditivity (NA) in protein-ligand binding where the change of two functional groups in one molecule results in much higher or lower activity than expected from the respective single changes. Identifying nonlinear cases and possible underlying explanations is crucial for a drug design project since it might influence which lead to follow. By systematically analyzing all AstraZeneca (AZ) inhouse compound data and publicly available ChEMBL25 bioactivity data, we show significant NA events in almost every second assay among the inhouse and once in every third assay in public data sets. Furthermore, 9.4% of all compounds of the AZ database and 5.1% from public sources display significant additivity shifts indicating important SAR features or fundamental measurement errors. Using NA data in combination with machine learning showed that nonadditive data is challenging to predict and even the addition of nonadditive data into training did not result in an increase in predictivity. Overall, NA analysis should be applied on a regular basis in many areas of computational chemistry and can further improve rational drug design.

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.

The Playbooks of Medicinal Chemistry Design Moves

Large databases of biologically relevant molecules, such as ChEMBL, SureChEMBL, or compound collections of pharmaceutical or agrochemical companies, are invaluable sources of medicinal chemistry information, albeit implicit. We developed a modified matched molecular pair approach to systematically and exhaustively extract the transformations in these databases and distill them into snippets of explicit design knowledge that are easily interpretable and directly applicable. The resulting "playbooks of medicinal chemistry design moves" capture the collective pharmaceutical and agrochemical research expertise across multiple chemists, companies, targets, and projects. They can be queried in an automated fashion for systematic prospective design and compound generation. The ChEMBL playbook and an application to exploit it are available at https://github.com/mahendra-awale/medchem_moves.

Crystal structure of Leishmania mexicana cysteine protease B in complex with a high-affinity azadipeptide nitrile inhibitor

Leishmania mexicana is an obligate intracellular protozoan parasite that causes the cutaneous form of leishmaniasis affecting South America and Mexico. The cysteine protease LmCPB is essential for the virulence of the parasite and therefore, it is an appealing target for antiparasitic therapy. A library of nitrile-based cysteine protease inhibitors was screened against LmCPB to develop a treatment of cutaneous leishmaniasis. Several compounds are sufficiently high-affinity LmCPB inhibitors to serve both as starting points for drug discovery projects and as probes for target validation. A 1.4 Å X ray crystal structure, the first to be reported for LmCPB, was determined for the complex of this enzyme covalently bound to an azadipeptide nitrile ligand. Mapping the structure-activity relationships for LmCPB inhibition revealed superadditive effects for two pairs of structural transformations. Therefore, this work advances our understanding of azadipeptidyl and dipeptidyl nitrile structure-activity relationships for LmCPB structure-based inhibitor design. We also tested the same series of inhibitors on related cysteine proteases cathepsin L and Trypanosoma cruzi cruzain. The modulation of these mammalian and protozoan proteases represents a new framework for targeting papain-like cysteine proteases.

Allosteric Mechanisms of Nonadditive Substituent Contributions to Protein-Ligand Binding

Quantifying chemical substituent contributions to ligand-binding free energies is challenging due to nonadditive effects. Protein allostery is a frequent cause of nonadditivity, but the underlying allosteric mechanisms often remain elusive. Here, we propose a general NMR-based approach to elucidate such mechanisms and we apply it to the HCN4 ion channel, whose cAMP-binding domain is an archetypal conformational switch. Using NMR, we show that nonadditivity arises not only from concerted conformational transitions, but also from conformer-specific effects, such as steric frustration. Our results explain how affinity-reducing functional groups may lead to affinity gains if combined. Surprisingly, our approach also reveals that nonadditivity depends markedly on the receptor conformation. It is negligible for the inhibited state but highly significant for the active state, opening new opportunities to tune potency and agonism of allosteric effectors.

FEP+ calculations predict a stereochemical SAR switch for first-in-class indoline NIK inhibitors for multiple myeloma

In the search for first-in-class NIK inhibitors for multiple myeloma, we discovered an azaindoline hit class generated from a biochemical NIK autophosphorylation high-throughput screening assay which was optimized to the final cyanoindoline compound class. During the hit-to-lead phase, a prominent stereochemical SAR switch was observed which was accurately predicted by in silico FEP+ calculations. Crystallographic and computational analysis showed that for both stereoisomers comparable contacts, both in nature and number, could be formed by the switching hydroxyl group, making this system particularly interesting from an interaction energy viewpoint. We provide a detailed analysis of our FEP+ and WaterMap calculations and show how this type of computational chemistry methods are useful during hit-to-lead and lead optimization phases.

Optimization strategy of single-digit nanomolar cross-class inhibitors of mammalian and protozoa cysteine proteases

Cysteine proteases (CPs) are involved in a myriad of actions that include not only protein degradation, but also play an essential biological role in infectious and systemic diseases such as cancer. CPs also act as biomarkers and can be reached by active-based probes for diagnostic and mechanistic purposes that are critical in health and disease. In this paper, we present the modulation of a CP panel of parasites and mammals (Trypanosoma cruzi cruzain, LmCPB, CatK, CatL and CatS), whose inhibition by nitrile peptidomimetics allowed the identification of specificity and selectivity for a given CP. The activity cliffs identified at the CP inhibition level are useful for retrieving trends through multiple structure-activity relationships. For two of the cruzain inhibitors (10g and 4e), both enthalpy and entropy are favourable to Gibbs binding energy, thus overcoming enthalpy-entropy compensation (EEC). Group contribution of individual molecular modification through changes in enthalpy and entropy results in a separate partition on the relative differences of Gibbs binding energy (ΔΔG). Overall, this study highlights the role of CPs in polypharmacology and multi-target screening, which represents an imperative trend in the actual drug discovery effort.

Mapping the S1 and S1' subsites of cysteine proteases with new dipeptidyl nitrile inhibitors as trypanocidal agents

The cysteine protease cruzipain is considered to be a validated target for therapeutic intervention in the treatment of Chagas disease. A series of 26 new compounds were designed, synthesized, and tested against the recombinant cruzain (Cz) to map its S1/S1´ subsites. The same series was evaluated on a panel of four human cysteine proteases (CatB, CatK, CatL, CatS) and Leishmania mexicana CPB, which is a potential target for the treatment of cutaneous leishmaniasis. The synthesized compounds are dipeptidyl nitriles designed based on the most promising combinations of different moieties in P1 (ten), P2 (six), and P3 (four different building blocks). Eight compounds exhibited a Ki smaller than 20.0 nM for Cz, whereas three compounds met these criteria for LmCPB. Three inhibitors had an EC50 value of ca. 4.0 μM, thus being equipotent to benznidazole according to the antitrypanosomal effects. Our mapping approach and the respective structure-activity relationships provide insights into the specific ligand-target interactions for therapeutically relevant cysteine proteases.

Discovery of 3,3'-pyrrolidinyl-spirooxindoles as cardioprotectant prohibitin ligands

The scaffold proteins prohibitins-1 and 2 (PHB1/2) play many important roles in coordinating many cell signaling pathways and represent emerging targets in cardiology and oncology. We previously reported that a family of natural products derivatives, flavaglines, binds to PHB1/2 to exert cardioprotectant and anti-cancer effects. However, flavaglines also target the initiation factor of translation eIF4A, which doesn't contribute to cardioprotection and may even induce some adverse effects. Herein, we report the development of a convenient and robust synthesis of the new PHB2 ligand 2'-phenylpyrrolidinyl-spirooxindole, and its analogues. We discovered that these compounds displays cardioprotective effect against doxorubicin mediated cardiotoxicity and uncovered the structural requirement for this activity. We identified in particular some analogues that are more cardioprotectant than flavaglines. Pull-down experiments demonstrated that these compounds bind not only to PHB2 but also PHB1. These novel PHB ligands may provide the basis for the development of new drugs candidates to protect the heart against the adverse effects of anticancer treatments.

Nonadditivity Analysis

We introduce the statistics behind a novel type of SAR analysis named "nonadditivity analysis". On the basis of all pairs of matched pairs within a given data set, the approach analyzes whether the same transformations between related molecules have the same effect, i.e., whether they are additive. Assuming that the experimental uncertainty is normally distributed, the additivities can be analyzed with statistical rigor and sets of compounds can be found that show significant nonadditivity. Nonadditivity analysis can not only detect nonadditivity, potential SAR outliers, and sets of key compounds but also allow estimating an upper limit of the experimental uncertainty in the data set. We demonstrate how complex SAR features that inform medicinal chemistry can be found in large SAR data sets. Finally, we show how the upper limit of experimental uncertainty for a given biochemical assay can be estimated without the need for repeated measurements of the same protein-ligand system.

Synthesis and structure-activity relationship of nitrile-based cruzain inhibitors incorporating a trifluoroethylamine-based P2 amide replacement

The structure-activity relationship for nitrile-based cruzain inhibitors incorporating a P2 amide replacement based on trifluoroethylamine was explored by deconstruction of a published series of inhibitors. It was demonstrated that the P3 biphenyl substituent present in the published inhibitor structures could be truncated to phenyl with only a small loss of affinity. The effects of inverting the configuration of the P2 amide replacement and linking a benzyl substituent at P1 were observed to be strongly nonadditive. We show that plotting affinity against molecular size provides a means to visualize both the molecular size efficiency of structural transformations and the nonadditivity in the structure-activity relationship. We also show how the relationship between affinity and lipophilicity, measured by high-performance liquid chromatography with an immobilized artificial membrane stationary phase, may be used to normalize affinity with respect to lipophilicity.

The nature of ligand efficiency

Ligand efficiency is a widely used design parameter in drug discovery. It is calculated by scaling affinity by molecular size and has a nontrivial dependency on the concentration unit used to express affinity that stems from the inability of the logarithm function to take dimensioned arguments. Consequently, perception of efficiency varies with the choice of concentration unit and it is argued that the ligand efficiency metric is not physically meaningful nor should it be considered to be a metric. The dependence of ligand efficiency on the concentration unit can be eliminated by defining efficiency in terms of sensitivity of affinity to molecular size and this is illustrated with reference to fragment-to-lead optimizations. Group efficiency and fit quality are also examined in detail from a physicochemical perspective. The importance of examining relationships between affinity and molecular size directly is stressed throughout this study and an alternative to ligand efficiency for normalization of affinity with respect to molecular size is presented.

Decision Making in Medicinal Chemistry: The Power of Our Intuition

Medicinal chemists rely on their intuition to make decisions regarding the course of a medicinal chemistry program. Our ability to accurately and efficiently process large data sets routinely requires that we reduce the volume of information to manageable proportions. This prioritization process, however, can be affected by intuitive biases. One such situation is structure-activity relationship (SAR) analysis in nonadditive data sets in which attempts to intuitively predict the activity of compounds based on preliminary data can lead to erroneous conclusions. Matrix analysis can be a useful tool to accurately determine the nature of the SAR and to improve our decision-making process during an analoging campaign.

Mathematical and Structural Characterization of Strong Nonadditive Structure-Activity Relationship Caused by Protein Conformational Changes

In medicinal chemistry, accurate prediction of additivity-based structure-activity relationship (SAR) analysis rests on three assumptions: (1) a consistent binding pose of the central scaffold, (2) no interaction between the R group substituents, and (3) a relatively rigid binding pocket in which the R group substituents act independently. Previously, examples of nonadditive SAR have been documented in systems that deviate from the first two assumptions. Local protein structural change upon ligand binding, through induced fit or conformational selection, although a well-known phenomenon that invalidates the third assumption, has not been linked to nonadditive SAR conclusively. Here, for the first time, we present clear structural evidence that the formation of a hydrophobic pocket upon ligand binding in PDE2 catalytic site reduces the size of another distinct subpocket and contributes to strong nonadditive SAR between two otherwise distant R groups.

Automating drug discovery

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction

Matched molecular series (MMS) analysis is an extension of matched molecular pair (MMP) analysis where all of the MMPs belong to the same chemical series. An MMS within a biological assay is able to capture specific structure activity relationships resulting from chemical substitution at a single location in the molecule. Under this convention, an MMS has the ability to capture one specific interaction vector between the compounds in a series and their therapeutic target. MMS analysis has the potential to translate the SAR from one series to another even across different protein targets or assays. A significant limitation of this approach is the lack of chemical series with a sufficient number of overlapping fragments to establish a statistically strong SAR in most databases. This results in either an inability to perform MMS analysis altogether or a potentially high proportion of spurious matches from chance correlations when the MMS compound count is low. This paper presents the novel concept of an MMS Network, which captures the SAR relationships between a set of related MMSs and significantly enhances the performance of MMS analysis by reducing the number of spurious matches leading to the identification of unexpected and potentially transferable SAR across assays. The results of a full retrospective leave-one-out analysis and randomization simulation are provided, and examples of pharmaceutically relevant programs will be presented to demonstrate the potential of this method.

Binding Pose Flip Explained via Enthalpic and Entropic Contributions

The anomalous binding modes of five highly similar fragments of TIE2 inhibitors, showing three distinct binding poses, are investigated. We report a quantitative rationalization for the changes in binding pose based on molecular dynamics simulations. We investigated five fragments in complex with the transforming growth factor β receptor type 1 kinase domain. Analyses of these simulations using Grid Inhomogeneous Solvation Theory (GIST), pK_A calculations, and a tool to investigate enthalpic differences upon binding unraveled the various thermodynamic contributions to the different binding modes. While one binding mode flip can be rationalized by steric repulsion, the second binding pose flip revealed a different protonation state for one of the ligands, leading to different enthalpic and entropic contributions to the binding free energy. One binding pose is stabilized by the displacement of entropically unfavored water molecules (binding pose determined by solvation entropy), ligands in the other binding pose are stabilized by strong enthalpic interactions, overcompensating the unfavorable water entropy in this pose (binding pose determined by enthalpic interactions). This analysis elucidates unprecedented details determining the flipping of the binding modes, which can elegantly explain the experimental findings for this system.

Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations

Molecular matched pair (MMP) analysis has been used for more than 40 years within molecular design and is still an important tool to analyse potency data and other compound properties. The methods used to find matched pairs range from manual inspection, through supervised methods to unsupervised methods, which are able to find previously unknown molecular pairs. Recent publications demonstrate the value of automatic MMP analysis of publicly available bioactivity databases. The MMP concept has its limitations, but because of its easy to use and intuitive nature, it will remain one of the most important tools in the toolbox of many drug designers.

Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design

Urea derivatives are ubiquitously found in many chemical disciplines. N,N'-substituted ureas may show different conformational preferences depending on their substitution pattern. The high energetic barrier for isomerization of the cis and trans state poses additional challenges on computational simulation techniques aiming at a reproduction of the biological properties of urea derivatives. Herein, we investigate energetics of urea conformations and their interconversion using a broad spectrum of methodologies ranging from data mining, via quantum chemistry to molecular dynamics simulation and free energy calculations. We find that the inversion of urea conformations is inherently slow and beyond the time scale of typical simulation protocols. Therefore, extra care needs to be taken by computational chemists to work with appropriate model systems. We find that both knowledge-driven approaches as well as physics-based methods may guide molecular modelers towards accurate starting structures for expensive calculations to ensure that conformations of urea derivatives are modeled as adequately as possible.

Nano-cuprous oxide catalyzed one-pot synthesis of a carbazole-based STAT3 inhibitor: a facile approach via intramolecular C–N bond formation reactions

In this study, we report the one-pot synthesis of substituted carbazole derivatives using nano cuprous oxide as a catalyst and demonstrated the STAT3 inhibitory activity of new compounds.

Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications

Biopharmaceuticals hold great promise for the future of drug discovery. Nevertheless, rational drug design strategies are mainly focused on the discovery of small synthetic molecules. Herein we present matched peptides, an innovative analysis technique for biological data related to peptide and protein sequences. It represents an extension of matched molecular pair analysis toward macromolecular sequence data and allows quantitative predictions of the effect of single amino acid substitutions on the basis of statistical data on known transformations. We demonstrate the application of matched peptides to a data set of major histocompatibility complex class II peptide ligands and discuss the trends captured with respect to classical quantitative structure–activity relationship approaches as well as structural aspects of the investigated protein–peptide interface. We expect our novel readily interpretable tool at the interface of cheminformatics and bioinformatics to support the rational design of biopharmaceuticals and give directions for further development of the presented methodology.