The extensive bioactivity data available in public databases, such as ChEMBL, has facilitated in-depth structure- activity relationship (SAR) analysis, which are essential for understanding the impact of molecular modifications on biological activity in a comprehensive manner. A central strategy in SAR analysis is the assessment of molecular similarity. Several approaches preferred by medicinal chemists have been developed to efficiently capture structurally related compounds on a large scale. Represented as a popular molecular editing strategy in hit-to- lead and lead optimization processes, we previously introduced four types of single-atom modifications (SAMs) as chemical similarity criterion and conducted a systematic analysis of their application in compound design. In this study, we expanded the analysis to cover 10 common SAMs, including carbon-nitrogen (N <-> C), O <-> C, N <-> O, S <-> O, as well as simpler modifications such as OH <-> H, CH3 <-> H, and halogen-hydrogen (F, Cl, Br, I <-> H) exchanges. Leveraging high-confidence bioactivity data from ChEMBL (version 34), we assembled a comprehensive dataset comprising 374,979 SAM pairs. Following an evaluation of the frequency of these SAM types in medicinal chemistry efforts, we focused on SAM-induced activity cliffs (ACs), yielding over 7400 ACs, substantially expanding the current knowledgebase of ACs associated with single-atom changes. Furthermore, structural analysis of these ACs, supported by experimental data, provides critical insights into the role of single- atom modifications in modulating compound activity, offering practical guidance for the structure-based optimization of molecular properties in drug development. As a result, we are providing open access to all identified ACs along with their associated structural information.

Poly(ADP-ribose) polymerase 1 (PARP1) inhibitors have revolutionized the treatment of many cancers with DNA-repairing deficiencies via synthetic lethality. Advocated by the polypharmacology concept, recent evidence discovered that a significantly synergistic effect in increasing the death of cancer cells was observed by simultaneously perturbating the enzymatic activities of bromodomain-containing protein 4 (BRD4) and PARP1. Here, we developed a novel cheminformatics approach combined with a structure-based method aiming to facilitate the design of dual PARP1-BRD4 inhibitors. Instead of linking pharmacophores, the developed approach first identified merged pharmacophores (a pool of amide-containing ring systems), from which phenanthridin-6(5H)-one was further prioritized. Based on this starting point, several small molecules were rationally designed, among which HF4 exhibited low micromolar inhibitory activity against BRD4 and PARP1, particularly exhibiting strong inhibition of BRD4 BD1 with an IC50 value of 204 nM. Furthermore, it demonstrated potent antiproliferative effects against breast cancer gene-deficient and proficient breast cancer cell lines by arresting cell cycle progression and impeding DNA damage repair. Collectively, our systematic efforts to design lead-like molecules have the potential to open doors for the exploration of dual PARP1-BRD4 inhibitors as a promising avenue for breast cancer treatment. Furthermore, the developed approach can be extended to systematically design inhibitors targeting PARP1 and other related targets.

The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery. However, some drug metabolites can reach high plasma concentrations and display in vivo activity. Here, we use computational and experimental methods to comprehensively characterize the kinase polypharmacology of M324, the major metabolite of the PARP1 inhibitor rucaparib. We demonstrate that M324 displays unique PLK2 inhibition at clinical concentrations. This kinase activity could have implications for the efficacy and safety of rucaparib and therefore warrants further clinical investigation. Importantly, we identify synergy between the drug and the metabolite in prostate cancer models and a complete reduction of α-synuclein accumulation in Parkinson’s disease models. These activities could be harnessed in the clinic or open new drug discovery opportunities. The study reported here highlights the importance of characterizing the activity of drug metabolites to comprehensively understand drug response in the clinic and exploit our current drug arsenal in precision medicine.

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine–associated receptor 1 (TAAR1), a G protein–coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 μM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.

A set of 2-aryl-9-H or methyl-6-morpholinopurine derivatives were synthesized and assayed through radioligand binding tests at human A1, A2A, A2B, and A3 adenosine receptor subtypes. Eleven purines showed potent antagonism at A1, A3, dual A1/A2A, A1/A2B, or A1/A3 adenosine receptors. Additionally, three compounds showed high affinity without selectivity for any specific adenosine receptor. The structure-activity relationships were made for this group of new compounds. The 9-methylpurine derivatives were generally less potent but more selective, and the 9H-purine derivatives were more potent but less selective. These compounds can be an important source of new biochemical tools and/or pharmacological drugs.

Drug-induced phospholipidosis (DIPL), characterized by excessive accumulation of phospholipids in lysosomes, can lead to clinical adverse effects. It may also alter phenotypic responses in functional studies using chemical probes. Therefore, robust methods are needed to predict and quantify phospholipidosis (PL) early in drug discovery and in chemical probe characterization. Here, we present a versatile high-content live-cell imaging approach, which was used to evaluate a chemogenomic and a lysosomal modulation library. We trained and evaluated several machine learning models using the most comprehensive set of publicly available compounds and interpreted the best model using SHapley Additive exPlanations (SHAP). Analysis of high-quality chemical probes extracted from the Chemical Probes Portal using our algorithm revealed that closely related molecules, such as chemical probes and their matched negative controls can differ in their ability to induce PL, highlighting the importance of identifying PL for robust target validation in chemical biology.

In this study, a virtual screening pipeline comprising ligand-based and structure-based approaches was established and applied for the identification of dual PTP1B and ACP1 inhibitors. As a result, a series of benzoic acid derivatives was discovered, and compound H3 and S6 demonstrated PTP1B and ACP1 inhibitory activity, with IC50 values of 3.5 and 8.2 mu M for PTP1B, and 2.5 and 5.2 mu M for ACP1, respectively. Molecular dynamics simulations illustrated that H3 interacted with critical residues in the active site, such as Cys215 and Arg221 for PTP1B, and Cys17 and Arg18 for ACP1. Enzymatic kinetic research indicated that identified inhibitors competitively inhibited PTP1B and ACP1. Additionally, cellular assays demonstrated that H3 and S6 effectively increased glucose uptake in insulin-resistant HepG2 cells while displaying very limited cytotoxicity at their effective concentrations. In summary, H3 and S6 represent novel dual-target inhibitors for PTP1B and ACP1, warranting further investigation as potential agents for the treatment of diabetes.

Given the increasing quest for selective kinase inhibitors, we have systematically investigated structural and structure-promiscuity relationships between promiscuous kinase inhibitors and other types with increasing potential for selective kinase inhibition. Therefore, inhibitors with different modes of action were extracted from X-ray structures of kinase complexes. For more than 18,000 promiscuous kinase inhibitors and 1253 type I1/2, II, and allosteric inhibitors with structurally confirmed mechanisms, analogue space was systematically charted. These inhibitors were active against a total of 426 human kinases. While nearly 80% of the promiscuous inhibitors formed related analogues series, only ~30% of other types of inhibitors were involved in such structural relationships and many of these inhibitors also had multi-kinase activity. Thus, most of the investigated type I1/2, II, and allosteric inhibitors with reported single-kinase activity were distinguished from promiscuous inhibitors, thus indicating potential for kinase selectivity. Structural relationships between promiscuous inhibitors and the subset of other inhibitors were organized in a matrix format including kinase activity profiles, revealing structure-promiscuity relationships for follow-up investigations.

A data set was generated comprising currently available competitive and allosteric human protein kinase inhibitors confirmed by X-ray crystallography. This data set has been used to systematically explore structural relationships between these types of inhibitors with different mechanisms of action. A major finding of this study has been that these different inhibitor types frequently displayed structural relationships and essentially represented a structural continuum [1]. Use of the data set is not limited to the inhibitor-centric exploration of structural relationships. The collection of kinase inhibitors with structurally confirmed distinct mechanisms of action can also be used, for example, to aid in structure-based drug design or the search for new allosteric kinase inhibitors.

The structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

Allosteric kinase inhibitors are thought to have high selectivity and are prime candidates for kinase drug discovery. In addition, the exploration of allosteric mechanisms represents an attractive topic for basic research and drug design. Although the identification and characterization of allosteric kinase inhibitors is still far from being routine, X-ray structures of kinase complexes have been determined for a significant number of such inhibitors. On the basis of structural data, allosteric inhibitors can be confirmed. We report a comprehensive survey of allosteric kinase inhibitors and activators from publicly available X-ray structures, map their binding sites, and determine their distribution over binding pockets in kinases. In addition, we discuss structural features of these compounds and identify active structural analogues and high-confidence target annotations, indicating additional activities for a subset of allosteric inhibitors. This contribution aims to provide a detailed structure-based view of allosteric kinase inhibition.

Very small chemical changes in active compounds causing large potency effects are of particular interest in medicinal chemistry and drug design. We have systematically searched active compounds with available high-confidence activity data for pairs of structural analogs with dual-atom replacements and additional analogs with corresponding single-atom replacements. From ~287,000 unique qualifying compounds with activity against nearly 1900 unique targets, ~3500 target-based analog pairs with dual-atom replacements were identified. These included 852 pairs with significant differences in compound potency, representing a set of previously unobserved activity cliffs. Comparing these pairs with corresponding single-atom replacement analogs, which were frequently identified, made it possible to systematically analyze how potency changes propagated from single- to dual-atom replacements. The analysis uncovered different potency effects and revealed that individual atom replacements were often decisive for activity cliff formation. For a limited number of activity cliffs, X-ray structures of targets in complex with cliff compounds were available, which aided in rationalizing potency alterations among analogs with single- or dual-atom replacements. The analog pairs identified herein provide a rich resource of structure-activity relationship information and attractive test cases for calibrating computational methods.

Allosteric and ATP-competitive kinase inhibitors act by distinct mechanisms and are expected to have high and low kinase selectivity, respectively. This also raises the question whether or not these different types of inhibitors might be structurally distinct. To address this question, we have assembled data sets of currently available competitive and allosteric kinase inhibitors confirmed by X-ray crystallography and systematically compared these compounds on the basis of different structural criteria. Many competitive and allosteric inhibitors were found to contain the same or similar substructures and a subset of allosteric inhibitors was found to share core structures with ATP site-directed inhibitors. In some instances, small chemical modifications of common cores were found to yield either allosteric or competitive inhibitors. Hence, these different categories of inhibitors with distinct mechanisms of action were often structurally related and represented much more of a structural continuum than discrete states. Additional target annotations were frequently identified for competitive inhibitors, but were rare for allosteric inhibitors. As a part of this study, our collection of kinase inhibitors and the associated information are made freely available to enable further assessment of chemical modifications that distinguish similar kinase inhibitors with distinct mechanisms of action.

In medicinal chemistry, activity cliffs (ACs) are considered as sources of critical structure-activity relationship (SAR) information. ACs are capable of revealing such SAR information because they are formed by pairs or groups of structural analogs that are distinguished by small chemical modifications leading to large variations in compound potency. Such modifications can reveal critically important substitution sites in analog series. Small AC-encoded chemical changes enable the identification of SAR determinants. In this work, we have searched medicinal chemistry data for most "subtle" ACs in which participating compounds are only distinguished by single-atom modifications. These ACs can be directly associated with lead optimization strategies such as positional atom scanning (atom "walks") or heteroatom replacements in ring structures. More than 1500 of these ACs with activity against a variety of targets were identified. To further explore newly identified ACs, we searched for X-ray structures of ligand-target complexes containing participating AC compounds. For a subset of subtle ACs, X-ray structures of complexes made it possible to examine effects of single-atom changes in light of well-defined ligand-target interactions. Since ACs capturing minimal chemical changes are of particular interest for lead optimization and drug design, we make all newly identified ACs and associated structural information freely available as an open access deposition.

Activity cliffs (ACs) consist of structurally similar compounds with a large difference in potency against their target. Accordingly, ACs introduce discontinuity in structure-activity relationships (SARs) and are a prime source of SAR information. In compound data sets, the vast majority of ACs are formed by differently sized groups of structurally similar compounds with large potency variations. As a consequence, many of these compounds participate in multiple ACs. This coordinated formation of ACs increases their SAR information content compared to ACs considered as individual compound pairs, but complicates AC analysis. In network representations, coordinated ACs give rise to clusters of varying size and topology, which can be interactively and computationally analyzed. While AC networks are indispensable tools to study coordinated ACs, they become difficult to navigate and interpret in the presence of clusters of increasing size and complex topologies. Herein, we introduce reduced network representations that transform AC networks into an easily interpretable format from which SAR information in the form of R-group tables can be readily obtained. The simplified network variant greatly improves the interpretability of large and complex AC networks and substantially supports SAR exploration.

The activity cliff (AC) concept is of comparable relevance for medicinal chemistry and chemoinformatics. An AC is defined as a pair of structurally similar compounds with a large potency difference against a given target. In medicinal chemistry, ACs are of interest because they reveal small chemical changes with large potency effects, a concept referred to as structure-activity relationship (SAR) discontinuity. Computationally, ACs can be systematically identified, going far beyond individual compound series considered during lead optimization. Large-scale analysis of ACs has revealed characteristic features across many different compound activity classes. The way in which the molecular similarity and potency difference criteria have been addressed for defining ACs distinguishes between different generations of ACs and mirrors the evolution of the AC concept. We discuss different stages of this evolutionary path and highlight recent advances in AC research.

Activity cliffs (ACs) are pairs of structurally similar or analogous active compounds with large differences in potency against the same target. For identifying and analyzing ACs, similarity and potency difference criteria must be determined and consistently applied. This can be done in various ways, leading to different types of ACs. In this work, we introduce a new category of ACs by combining different similarity criteria, including the formation of matched molecular pairs and structural isomer relationships. A systematic computational search identified such ACs in compounds with activity against a variety of targets. In addition to other ACs exclusively formed by structural isomers, the newly introduced category of ACs is rich in structure-activity relationship (SAR) information, straightforward to interpret from a chemical perspective, and further extends the current spectrum of ACs.

7465 activity cliffs (ACs) are provided including ACs with two substitution sites (ds-ACs), isomer ACs (iso-ACs), and ACs containing privileged substructures (PS-ACs). See readme.txt for further details.

Activity cliffs (ACs) are defined as pairs of structurally similar or analogous active compounds with large potency differences [1]. As such, they provide important information for the exploration of structure-activity relationships (SARs) and chemical optimization. We have introduced a new category of ACs capturing minimal (single-atom) chemical modifications and identified more than 1500 of such ACs in compounds with activity against a variety of target proteins [2]. ACs with single-atom modifications (sam_ACs) include "atom-replacement ACs" (ar_ACs) that contain a single-atom replacement (N to C (N-C), O-C, N-O, or S-O) at a given position and "atom-walk ACs" (aw_ACs), in which two analogs are only distinguished by the position of a single heteroatom (non-carbon atom). For a number of sam_ACs, X-ray structures of complexes between AC targets and AC compounds were identified, which made it possible to explore the formation of sam_ACs on the basis of well-defined ligand-target interactions [2]. Our collection of sam_ACs including associated chemical and X-ray structure information, as described herein, is made freely available.

Aim: Extending the public knowledge base of activity cliffs (ACs) with new categories of ACs having special structural characteristics. Methodology: Dual-site ACs, isomer ACs and ACs with privileged substructures are described and their systematic identification is detailed. Exemplary results & data: More than 7400 new ACs belonging to different categories with activity against more than 200 targets were identified and are made publicly available. Limitations & next steps: For dual-site ACs, limited numbers of isomers are available as structural analogs for rationalizing contributions to AC formation. The search for such analogs will continue. In addition, the target distribution of ACs containing privileged substructures will be further analyzed.