The European Lead Factory: Results from a decade of collaborative, public-private, drug discovery programs.

The European Lead Factory (ELF) is a consortium of universities and small and medium-sized enterprises (SMEs) dedicated to drug discovery, and the pharmaceutical industry. This unprecedented consortium provides high-throughput screening, triage, and hit validation, including to non-consortium members. The ELF library was created through a novel compound-sharing model between nine pharmaceutical companies and expanded through library synthesis by chemistry-specialized SMEs. The library has been screened against ∼270 different targets and 15 phenotypic assays, and hits have been developed to form the basis of patents and spin-off companies. Here, we review the outcome of screening campaigns of the ELF, including the performance and physicochemical properties of the library, identification of possible frequent hitter compounds, and the effectiveness of the compound-sharing model.

Flexible protein-ligand docking using the Fleksy protocol.

Considering protein plasticity is important in accurately predicting the three-dimensional geometry of protein-ligand complexes. Here, we present the first public release of our flexible docking tool Fleksy, which is able to consider both ligand and protein flexibility in the docking process. We describe the workflow and different features of the software and present its performance on two cross-docking benchmark datasets.

Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement.

Bioisosteres have been defined as structurally different molecules or substructures that can form comparable intermolecular interactions, and therefore, fragments that bind to similar protein structures exhibit a degree of bioisosterism. We present KRIPO (Key Representation of Interaction in POckets): a new method for quantifying the similarities of binding site subpockets based on pharmacophore fingerprints. The binding site fingerprints have been optimized to improve their performance for both intra- and interprotein family comparisons. A range of attributes of the fingerprints was considered in the optimization, including the placement of pharmacophore features, whether or not the fingerprints are fuzzified, and the resolution and complexity of the pharmacophore fingerprints (2-, 3-, and 4-point fingerprints). Fuzzy 3-point pharmacophore fingerprints were found to represent the optimal balance between computational resource requirements and the identification of potential replacements. The complete PDB was converted into a database comprising almost 300,000 optimized fingerprints of local binding sites together with their associated ligand fragments. The value of the approach is demonstrated by application to two crystal structures from the Protein Data Bank: (1) a MAP kinase P38 structure in complex with a pyridinylimidazole inhibitor (1A9U) and (2) a complex of thrombin with melagatran (1K22). Potentially valuable bioisosteric replacements for all subpockets of the two studied protein are identified.

Highly Selective Sub-Nanomolar Cathepsin S Inhibitors by Merging Fragment Binders with Nitrile Inhibitors.

Pharmacological inhibition of cathepsin S (CatS) allows for a specific modulation of the adaptive immune system and many major diseases. Here, we used NMR fragment screening and crystal structure-aided merging to synthesize novel, highly selective CatS inhibitors with picomolar enzymatic Ki values and nanomolar functional activity in human Raji cells. Noncovalent fragment hits revealed binding hotspots, while the covalent inhibitor structure-activity relationship enabled efficient potency optimization.

Fragment-Based Discovery of Novel Potent Sepiapterin Reductase Inhibitors.

Genome-wide-association studies in chronic low back pain patients identified sepiapterin reductase as a high interest target for developing new analgesics. Here we used 19F NMR fragment screening for the discovery of novel, ligand-efficient SPR inhibitors. We report the crystal structures of six chemically diverse inhibitors complexed with SPR, identifying relevant interactions and binding modes in the sepiapterin pocket. Exploration of our initial fragment screening hit led to double-digit nanomolar inhibitors of SPR with excellent ligand efficiency.

A flexible approach to induced fit docking.

We present Fleksy, a new approach to consider both ligand and receptor flexibility in small molecule docking. Pivotal to our method is the use of a receptor ensemble to describe protein flexibility. To construct these ensembles, we use a backbone-dependent rotamer library and implement the concept of interaction sampling. The latter allows the evaluation of different orientations of ambivalent interaction partners. The docking stage consists of an ensemble-based soft-docking experiment using FlexX-Ensemble, followed by an effective flexible receptor-ligand complex optimization using Yasara. Fleksy produces a set of receptor-ligand complexes ranked using a consensus scoring function combining docking scores and force field energies. Averaged over three cross-docking datasets, containing 35 different receptor-ligand complexes in total, Fleksy reproduces the observed binding mode within 2.0 A for 78% of the complexes. This compares favorably to the rigid receptor FlexX program, which on average reaches a success rate of 44% for these datasets.

Structural Determinants for the Binding of Morphinan Agonists to the µ-Opioid Receptor.

Atomistic descriptions of the µ-opioid receptor (µOR) noncovalently binding with two of its prototypical morphinan agonists, morphine (MOP) and hydromorphone (HMP), are investigated using molecular dynamics (MD) simulations. Subtle differences between the binding modes and hydration properties of MOP and HMP emerge from the calculations. Alchemical free energy perturbation calculations show qualitative agreement with in vitro experiments performed in this work: indeed, the binding free energy difference between MOP and HMP computed by forward and backward alchemical transformation is 1.2±1.1 and 0.8±0.8 kcal/mol, respectively, to be compared with 0.4±0.3 kcal/mol from experiment. Comparison with an MD simulation of µOR covalently bound with the antagonist ß-funaltrexamine hints to agonist-induced conformational changes associated with an early event of the receptor's activation: a shift of the transmembrane helix 6 relative to the transmembrane helix 3 and a consequent loss of the key R165-T279 interhelical hydrogen bond. This finding is consistent with a previous proposal suggesting that the R165-T279 hydrogen bond between these two helices indicates an inactive receptor conformation.

From activity cliffs to target-specific scoring models and pharmacophore hypotheses.

The role of activity cliffs in drug discovery projects is certainly two-edged: on the one hand, they often lead to the failure of QSAR modeling techniques; on the other, they are highly valuable for identifying key aspects of SARs. In the presence of activity cliffs the results of purely ligand-based QSAR approaches often remain puzzling, and the resulting models have limited predictive power. Herein we present a new approach for the identification of structure-based activity cliffs (ISAC). It uses the valuable information of activity cliffs in a structure-based design scenario by analyzing interaction energies of protein-ligand complexes. Using the relative frequency at which a protein atom is involved in activity cliff events, we introduce a novel visualization of hot spots in the active site of a protein. The ISAC approach supports the medicinal chemist in elucidating the key interacting atoms of the binding site and facilitates the development of pharmacophore hypotheses. The hot spot visualization can be applied to small data sets in early project phases as well as in the lead optimization process. Based on the ISAC approach, we developed a method to derive target-specific scoring functions and pharmacophore constraints, which were validated on independent external data sets in virtual screening experiments. The activity-cliff-based approach shows an improved enrichment over the generic empirical scoring function for various protein targets in the validation set.

SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites.

Predictions of potential metabolites based on chemical structure are becoming increasingly important in drug discovery to guide medicinal chemistry efforts that address metabolic issues and to support experimental metabolite screening and identification. Herein we present a novel rule-based method, SyGMa (Systematic Generation of potential Metabolites), to predict the potential metabolites of a given parent structure. A set of reaction rules covering a broad range of phase 1 and phase 2 metabolism has been derived from metabolic reactions reported in the Metabolite Database to occur in humans. An empirical probability score is assigned to each rule representing the fraction of correctly predicted metabolites in the training database. This score is used to refine the rules and to rank predicted metabolites. The current rule set of SyGMa covers approximately 70 % of biotransformation reactions observed in humans. Evaluation of the rule-based predictions demonstrated a significant enrichment of true metabolites in the top of the ranking list: while in total, 68 % of all observed metabolites in an independent test set were reproduced by SyGMa, a large part, 30 % of the observed metabolites, were identified among the top three predictions. From a subset of cytochrome P450 specific metabolites, 84 % were reproduced overall, with 66 % in the top three predicted phase 1 metabolites. A similarity analysis of the reactions present in the database was performed to obtain an overview of the metabolic reactions predicted by SyGMa and to support ongoing efforts to extend the rules. Specific examples demonstrate the use of SyGMa in experimental metabolite identification and the application of SyGMa to suggest chemical modifications that improve the metabolic stability of compounds.

The quest for bioisosteric replacements.

To help advance drug discovery projects, a new and validated search method is presented by which potential bioisosteric replacements can be retrieved from a database of more than 700,000 structural fragments. The heart of the search method is an optimized topological pharmacophore fingerprint which describes each fragment as a combination of attachment points, hydrogen bond donors and acceptors, hydrophobic centers, conjugated atoms, and non-hydrogen atoms. In the fingerprint the influence of the attachment point is enhanced by giving it extra weight relative to the other descriptors. The Euclidean distance has proven to be the optimum distance measure to compare the fingerprints in a database search. The performance of the pharmacophore fingerprint based search method has been validated using more than 2200 bioisosteric fragment pairs extracted in an unbiased procedure from the BIOSTER database. The true bioisosteric pairs have been compared with pairs of random fragments originating from the WDI database. Normalized by the standard deviation of the random pairs distance distributions, an excellent separation of true pairs from random pairs was obtained for R-group fragments (2.2 standard deviation units) as well as for linkers (2.6 units) and cores (2.6 units). The bioisoster search method has been implemented as an intranet application called IBIS and is now routinely used by Organon researchers.