The Identification of New c-FLIP Inhibitors for Restoring Apoptosis in TRAIL-Resistant Cancer Cells.

The catalytically inactive caspase-8-homologous protein, c-FLIP, is a potent antiapoptotic protein highly expressed in various types of cancers. c-FLIP competes with caspase-8 for binding to the adaptor protein FADD (Fas-Associated Death Domain) following death receptors' (DRs) activation via the ligands of the TNF-R family. As a consequence, the extrinsic apoptotic signaling pathway involving DRs is inhibited. The inhibition of c-FLIP activity in tumor cells might enhance DR-mediated apoptosis and overcome immune and anticancer drug resistance. Based on an in silico approach, the aim of this work was to identify new small inhibitory molecules able to bind selectively to c-FLIP and block its anti-apoptotic activity. Using a homology 3D model of c-FLIP, an in silico screening of 1880 compounds from the NCI database (National Cancer Institute) was performed. Nine molecules were selected for in vitro assays, based on their binding affinity to c-FLIP and their high selectivity compared to caspase-8. These molecules selectively bind to the Death Effector Domain 2 (DED2) of c-FLIP. We have tested in vitro the inhibitory effect of these nine molecules using the human lung cancer cell line H1703, overexpressing c-FLIP. Our results showed that six of these newly identified compounds efficiently prevent FADD/c-FLIP interactions in a molecular pull-down assay, as well as in a DISC immunoprecipitation assay. The overexpression of c-FLIP in H1703 prevents TRAIL-mediated apoptosis; however, a combination of TRAIL with these selected molecules significantly restored TRAIL-induced cell death by rescuing caspase cleavage and activation. Altogether, our findings indicate that new inhibitory chemical molecules efficiently prevent c-FLIP recruitment into the DISC complex, thus restoring the caspase-8-dependent apoptotic cascade. These results pave the way to design new c-FLIP inhibitory molecules that may serve as anticancer agents in tumors overexpressing c-FLIP.

Highlighting the Major Role of Cyclin C in Cyclin-Dependent Kinase 8 Activity through Molecular Dynamics Simulations.

Dysregulation of cyclin-dependent kinase 8 (CDK8) activity has been associated with many diseases, including colorectal and breast cancer. As usual in the CDK family, the activity of CDK8 is controlled by a regulatory protein called cyclin C (CycC). But, while human CDK family members are generally activated in two steps, that is, the binding of the cyclin to CDK and the phosphorylation of a residue in the CDK activation loop, CDK8 does not require the phosphorylation step to be active. Another peculiarity of CDK8 is its ability to be associated with CycC while adopting an inactive form. These specificities raise the question of the role of CycC in the complex CDK8-CycC, which appears to be more complex than the other members of the CDK family. Through molecular dynamics (MD) simulations and binding free energy calculations, we investigated the effect of CycC on the structure and dynamics of CDK8. In a second step, we particularly focused our investigation on the structural and molecular basis of the protein-protein interaction between the two partners by finely analyzing the energetic contribution of residues and simulating the transition between the active and the inactive form. We found that CycC has a stabilizing effect on CDK8, and we identified specific interaction hotspots within its interaction surface compared to other human CDK/Cyc pairs. Targeting these specific interaction hotspots could be a promising approach in terms of specificity to effectively disrupt the interaction between CDK8. The simulation of the conformational transition from the inactive to the active form of CDK8 suggests that the residue Glu99 of CycC is involved in the orientation of three conserved arginines of CDK8. Thus, this residue may assume the role of the missing phosphorylation step in the activation mechanism of CDK8. In a more general view, these results point to the importance of keeping the CycC in computational studies when studying the human CDK8 protein in both the active and the inactive form.

Randomized double-blind placebo-controlled cosmetic trial of a topical first-in-class Neutraligand targeting the chemokine TARC/CCL17 in mild-to-moderate atopic dermatitis.

BACKGROUND: Atopic dermatitis has a marked economic impact and affects the quality of life. A cosmetic compound with an innovative strategy is proposed here as a small chemical neutraligand, GPN279 (previously identified as a theophylline derivative), that binds and potently neutralizes the TARC/CCL17 chemokine, activating the Th2 cell-expressed CCR4 receptor. OBJECTIVE: Our objective was to evaluate the safety and activity of topically applied GPN279 in mild-to-moderate atopic dermatitis patients in a randomized, double-blind, placebo-controlled, parallel group trial. Such cosmetic active ingredient targeting dry skin with an atopic tendency would open a parallel strategy to the pharmaceutical approach, in particular for mild to moderate subjects, as an alternative to reduce the evolution towards severe forms of atopy. METHODS: This 4-week trial included adults with mild-to-moderate atopic dermatitis, according to the SCORAD index. Patients were randomized into two groups treated by topical applications of either an emulsion containing 0.44% GPN279 in placebo on skin lesions or the placebo (4.56% glycerin). Clinical activity was evaluated with the SCORAD as the primary objective. As secondary objectives, POEM, erythema, skin moisturization, its barrier function (TEWL) and safety were evaluated. RESULTS: Twenty-one patients in each group completed the study. SCORAD was significantly improved in the GPN279 group vs. placebo. GPN279 also significantly improved POEM, induced a rapid and significant decrease of erythema, and improved skin moisture. GPN279 and placebo were well tolerated throughout the study. CONCLUSION: A cosmetic cream comprising the CCL17 neutraligand GPN279 improved the skin barrier and physiology criteria in patients with mild-to-moderate atopic dermatitis.

GÉNÉRALITÉS: La dermatite atopique a un impact économique marqué et affecte la qualité de vie. Un composé cosmétique dote d'une stratégie innovante est proposé ici sous la forme d'un petit neutraligand chimique, le GPN279 (précédemment identifié comme un dérivé de la théophylline), qui se lie et neutralise puissamment la chimiokine TARC/CCL17, activant le récepteur CCR4 exprimé par les cellules Th2. OBJECTIF: Notre objectif était d'évaluer l'innocuité et l'activité du GPN279 appliqué localement chez des patients atteints de dermatite atopique légère à modérée dans un essai randomisé, en double aveugle contre placebo et en groupes parallèles. Un tel actif cosmétique ciblant les peaux sèches à tendance atopique ouvrirait une stratégie parallèle à l'approche pharmaceutique, notamment pour les sujets atteints de forme légère à modérée, comme alternative visant à réduire l'évolution vers des formes sévères d'atopie. MÉTHODES: Cet essai de 4 semaines incluait des adultes atteints de dermatite atopique légère à modérée, selon l'indice SCORAD. Les patients ont été randomisés en deux groupes traités par application topique sur les lésions cutanées soit d'une émulsion contenant 0,44% de GPN279 dans un placebo, soit du placebo seul (4,56% de glycérine). L'activité clinique a été évaluée selon l'indice SCORAD comme objectif principal. Les objectifs secondaires évaluaient le POEM, l'érythème, l'hydratation de la peau, sa fonction barrière (TEWL) et la sécurité. RÉSULTATS: Vingt et un patients de chaque groupe ont terminé l'étude. L'indice SCORAD a été significativement amélioré dans le groupe GPN279 par rapport au placebo. Le GPN279 a également amélioré de manière significative le POEM, a induit une diminution rapide et significative de l'érythème et amélioré l'hydratation de la peau. Le GPN279 et le placebo ont été bien tolérés tout au long de l'étude. CONCLUSION: Une crème cosmétique contenant le neutraligand CCL17 GPN279 améliore la barrière cutanée et les critères physiologiques chez les patients atteints de dermatite atopique légère à modérée.

The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.

Estimation of Drug-Target Residence Time by Targeted Molecular Dynamics Simulations.

Drug-target residence time has emerged as a key selection factor in drug discovery since the binding duration of a drug molecule to its protein target can significantly impact its in vivo efficacy. The challenge in studying the residence time, in early drug discovery stages, lies in how to cost-effectively determine the residence time for the systematic assessment of compounds. Currently, there is still a lack of computational protocols to quickly estimate such a measure, particularly for large and flexible protein targets and drugs. Here, we report an efficient computational protocol, based on targeted molecular dynamics, to rank drug candidates by their residence time and to obtain insights into ligand-target dissociation mechanisms. The method was assessed on a dataset of 10 arylpyrazole inhibitors of CDK8, a large, flexible, and clinically important target, for which the experimental residence time of the inhibitors ranges from minutes to hours. The compounds were correctly ranked according to their estimated residence time scores compared to their experimental values. The analysis of protein-ligand interactions along the dissociation trajectories highlighted the favorable contribution of hydrophobic contacts to residence time and revealed key residues that strongly affect compound residence time.

Design and biological evaluation of substituted 5,7-dihydro-6H-indolo[2,3-c]quinolin-6-one as novel selective Haspin inhibitors.

A library of substituted indolo[2,3-c]quinolone-6-ones was developed as simplified Lamellarin isosters. Synthesis was achieved from indole after a four-step pathway sequence involving iodination, a Suzuki-Miyaura cross-coupling reaction, and a reduction/lactamization sequence. The inhibitory activity of the 22 novel derivatives was assessed on Haspin kinase. Two of them possessed an IC50 of 1 and 2 nM with selectivity towards a panel of 10 other kinases including the parent kinases DYRK1A and CLK1. The most selective compound exerted additionally a very interesting cell effect on the osteosarcoma U-2 OS cell line.

Design, Synthesis and SAR in 2,4,7-Trisubstituted Pyrido[3,2-d]Pyrimidine Series as Novel PI3K/mTOR Inhibitors.

This work describes the synthesis, enzymatic activities on PI3K and mTOR, in silico docking and cellular activities of various uncommon 2,4,7 trisubstituted pyrido[3,2-d]pyrimidines. The series synthesized offers a chemical diversity in C-7 whereas C-2 (3-hydroxyphenyl) and C-4 groups (morpholine) remain unchanged, in order to provide a better understanding of the molecular determinants of PI3K selectivity or dual activity on PI3K and mTOR. Some C-7 substituents were shown to improve the efficiency on kinases compared to the 2,4-di-substituted pyrimidopyrimidine derivatives used as references. Six novel derivatives possess IC50 values on PI3Kα between 3 and 10 nM. The compounds with the best efficiencies on PI3K and mTOR induced micromolar cytotoxicity on cancer cell lines possessing an overactivated PI3K pathway.

Residence Time Prediction of Type 1 and 2 Kinase Inhibitors from Unbinding Simulations.

In the early stage of a drug discovery process, the selection and optimization of a ligand is mainly based on equilibrium thermodynamic constants such as KD or IC50 values, which are representatives of the affinity of the compound for its target. However, these criteria are not able to correctly evaluate the efficacy of compounds in vivo and result in many failures of compound development during phase II of clinical trials. Residence time (RT) is an important parameter associated to an in vivo drug's safety and efficacy. The determination or modulation of kinetic rates correlated to RT may be performed to identify the best drug candidates in the early stages of a drug design project. For this purpose, a number of experimental methodologies were developed but remain costly in both time and money. Herein, we developed a novel protocol based on biased molecular dynamics simulations and transition-state theory in order to predict relative ligand kinetic rates and relative RTs of a series of compounds. First, we have repeatedly simulated the unbinding process of the ligand from its binding site to the outside of the target. Next, we sample the conformational space along the determined unbinding paths to allow relevant statistical distributions of the system. The free energy profiles associated to these distributions are then computed and used to predict the kinetics parameters. The studied set was composed of eight ligands with fast, intermediate, and slow dissociation rates and binding to the active and inactive states of p38α protein kinase. The proposed method provides an excellent correlation between the predicted values and the experimentally measured kinetic rates, in addition to a detailed characterization of the kinetic paths at the atomic level.

Design of new disubstituted imidazo[1,2-b]pyridazine derivatives as selective Haspin inhibitors. Synthesis, binding mode and anticancer biological evaluation.

Haspin is a mitotic protein kinase required for proper cell division by modulating Aurora B kinase localisation and activity as well as histone phosphorylation. Here a series of imidazopyridazines based on the CHR-6494 and Structure Activity Relationship was established. An assessment of the inhibitory activity of the lead structures on human Haspin and several other protein kinases is presented. The lead structure was rapidly optimised using a combination of crystal structures and effective docking models, with the best inhibitors exhibiting potent inhibitory activity on Haspin with IC50 between 6 and 100 nM in vitro. The developed inhibitors displayed anti-proliferative properties against various human cancer cell lines in 2D and spheroid cultures and significantly inhibited the migration ability of osteosarcoma U-2 OS cells. Notably, we show that our lead compounds are powerful Haspin inhibitors in human cells, and did not block G2/M cell cycle transition due to improved selectivity against CDK1/CyclinB.

Comparative Assessment of Protein Kinase Inhibitors in Public Databases and in PKIDB.

Since the first approval of a protein kinase inhibitor (PKI) by the Food and Drug Administration (FDA) in 2001, 55 new PKIs have reached the market, and many inhibitors are currently being evaluated in clinical trials. This is a clear indication that protein kinases still represent major drug targets for the pharmaceutical industry. In a previous work, we have introduced PKIDB, a publicly available database, gathering PKIs that have already been approved (Phase 4), as well as those currently in clinical trials (Phases 0 to 3). This database is updated frequently, and an analysis of the new data is presented here. In addition, we compared the set of PKIs present in PKIDB with the PKIs in early preclinical studies found in ChEMBL, the largest publicly available chemical database. For each dataset, the distribution of physicochemical descriptors related to drug-likeness is presented. From these results, updated guidelines to prioritize compounds for targeting protein kinases are proposed. The results of a principal component analysis (PCA) show that the PKIDB dataset is fully encompassed within all PKIs found in the public database. This observation is reinforced by a principal moments of inertia (PMI) analysis of all molecules. Interestingly, we notice that PKIs in clinical trials tend to explore new 3D chemical space. While a great majority of PKIs is located on the area of "flatland", we find few compounds exploring the 3D structural space. Finally, a scaffold diversity analysis of the two datasets, based on frequency counts was performed. The results give insight into the chemical space of PKIs, and can guide researchers to reach out new unexplored areas. PKIDB is freely accessible from the following website: http://www.icoa.fr/pkidb.

In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions.

Peptide⁻protein interactions are corner-stones of living functions involved in essential mechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural biology data, prediction of those interactions is of crucial interest for the rational development of new drugs, notably to fight diseases, such as cancer or Alzheimer's disease. Because of the high flexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein cavity remains challenging. Several theoretical approaches have been developed in the last decade to address this issue. Nevertheless, improvements are needed, such as the conformation prediction of peptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel in silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptide⁻protein interactions. In order to reduce the conformational space, this innovative method splits peptides into several short segments. Then, it uses the performance of intramolecular covalent docking to rebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is constructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set of 11 crystallized peptide⁻protein complexes, the IRDL method shows promising results, since it is able to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) below 2 Šin the top five ranked solutions. For some complexes, IRDL method outperforms two other docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug design projects to predict peptide⁻protein interactions.

Thermodynamic enantioseparation behavior of phenylthiohydantoin-amino acid derivatives in supercritical fluid chromatography on polysaccharide chiral stationary phases.

Thirteen pairs of enantiomers belonging to the same structural family (phenylthiohydantoin-amino acids) were analyzed on two polysaccharide chiral stationary phases, namely, tris-(3,5-dimethylphenylcarbamate) of amylose (Chiralpak AD-H) or cellulose (Chiralcel OD-H) in supercritical fluid chromatography with a carbon dioxide/methanol mobile phase (90:10 v/v). Five different temperatures (5, 10, 20, 30, 40°C) were applied to evaluate the thermodynamic behavior of these enantioseparations. On the cellulose stationary phase, the retention, and separation trends were most similar among the set of probe analytes, suggesting that the chiral cavities in this stationary phase have little diversity, or that all analytes accessed the same cavities. Conversely, the retention and separation trends on the amylose phase were much more diverse, and could be related to structural differences among the set of probe analytes (carbon chain length in the amino acid residue, secondary amine in proline, existence of covalent rings, or formation of pseudo-rings via intramolecular hydrogen bonds). The large variability of behaviors on the amylose phase suggests that the chiral-binding sites in this chiral stationary phase have more variety than on the cellulose phase, and that the analytes did access different cavities.

PKIDB: A Curated, Annotated and Updated Database of Protein Kinase Inhibitors in Clinical Trials.

The number of protein kinase inhibitors (PKIs) approved worldwide continues to grow steadily, with 39 drugs approved in the period between 2001 and January 2018. PKIs on the market have been the subject of many reviews, and structure-property relationships specific to this class of drugs have been inferred. However, the large number of PKIs under development is often overlooked. In this paper, we present PKIDB (Protein Kinase Inhibitor Database), a monthly-updated database gathering approved PKIs as well as PKIs currently in clinical trials. The database compiles currently 180 inhibitors ranging from phase 0 to 4 clinical trials along with annotations extracted from seven public resources. The distribution and property ranges of standard physicochemical properties are presented. They can be used as filters to better prioritize compound selection for future screening campaigns. Interestingly, more than one-third of the kinase inhibitors violate at least one Lipinski's rule. A Principal Component Analysis (PCA) reveals that Type-II inhibitors are mapped to a distinct chemical space as compared to orally administrated drugs as well as to other types of kinase inhibitors. Using a Principal Moment of Inertia (PMI) analysis, we show that PKIs under development tend to explore new shape territories as compared to approved PKIs. In order to facilitate the analysis of the protein space, the kinome tree has been annotated with all protein kinases being targeted by PKIs. Finally, we analyzed the pipeline of the pharmaceutical companies having PKIs on the market or still under development. We hope that this work will assist researchers in the kinase field in identifying and designing the next generation of kinase inhibitors for still untargeted kinases. The PKIDB database is freely accessible from a website at http://www.icoa.fr/pkidb and can be easily browsed through a user-friendly spreadsheet-like interface.

Development of Kinase Inhibitors via Metal-Catalyzed C⁻H Arylation of 8-Alkyl-thiazolo[5,4-f]-quinazolin-9-ones Designed by Fragment-Growing Studies.

Efficient metal catalyzed C⁻H arylation of 8-alkyl-thiazolo[5,4-f]-quinazolin-9-ones was explored for SAR studies. Application of this powerful chemical tool at the last stage of the synthesis of kinase inhibitors allowed the synthesis of arrays of molecules inspired by fragment-growing studies generated by molecular modeling calculations. Among the potentially active compounds designed through this strategy, FC162 (4c) exhibits nanomolar IC50 values against some kinases, and is the best candidate for the development as a DYRK kinase inhibitor.

The use of novel selectivity metrics in kinase research.

BACKGROUND: Compound selectivity is an important issue when developing a new drug. In many instances, a lack of selectivity can translate to increased toxicity. Protein kinases are particularly concerned with this issue because they share high sequence and structural similarity. However, selectivity may be assessed early on using data generated from protein kinase profiling panels. RESULTS: To guide lead optimization in drug discovery projects, we propose herein two new selectivity metrics, namely window score (WS) and ranking score (RS). These metrics can be applied to standard in vitro data-including intrinsic enzyme activity/affinity (Ki, IC50 or percentage of inhibition), cell-based potency (percentage of effect, EC50) or even kinetics data (Kd, Kon and Koff). They are both easy to compute and offer different viewpoints from which to consider compound selectivity. CONCLUSIONS: We performed a comparative analysis of their respective performance on several data sets against already published selectivity metrics and analyzed how they might influence compound selection. Our results showed that the two new metrics bring additional information to prioritize compound selection. Two novel metrics were developed to better estimate selectivity of compounds screened on multiple proteins.

Prediction of Protein Kinase-Ligand Interactions through 2.5D Kinochemometrics.

So far, 518 protein kinases have been identified in the human genome. They share a common mechanism of protein phosphorylation and are involved in many critical biological processes of eukaryotic cells. Deregulation of the kinase phosphorylation function induces severe illnesses such as cancer, diabetes, or inflammatory diseases. Many actors in the pharmaceutical domain have made significant efforts to design potent and selective protein kinase inhibitors as new potential drugs. Because the ATP binding site is highly conserved in the protein kinase family, the design of selective inhibitors remains a challenge and has negatively impacted the progression of drug candidates to late-stage clinical development. The work presented here adopts a 2.5D kinochemometrics (KCM) approach, derived from proteochemometrics (PCM), in which protein kinases are depicted by a novel 3D descriptor and the ligands by 2D fingerprints. We demonstrate in two examples that the protein descriptor successfully classified protein kinases based on their group membership and their Asp-Phe-Gly (DFG) conformation. We also compared the performance of our models with those obtained from a full 2D KCM model and QSAR models. In both cases, the internal validation of the models demonstrated good capabilities to distinguish "active" from "inactive" protein kinase-ligand pairs. However, the external validation performed on two independent data sets showed that the two statistical models tended to overestimate the number of "inactive" pairs.

Limited Proteolysis Combined with Stable Isotope Labeling Reveals Conformational Changes in Protein (Pseudo)kinases upon Binding Small Molecules.

Likely due to conformational rearrangements, small molecule inhibitors may stabilize the active conformation of protein kinases and paradoxically promote tumorigenesis. We combined limited proteolysis with stable isotope labeling MS to monitor protein conformational changes upon binding of small molecules. Applying this method to the human serine/threonine kinase B-Raf, frequently mutated in cancer, we found that binding of ATP or its nonhydrolyzable analogue AMP-PNP, but not ADP, stabilized the structure of both B-Raf(WT) and B-Raf(V600E). The ATP-competitive type I B-Raf inhibitor vemurafenib and the type II inhibitor sorafenib stabilized the kinase domain (KD) but had distinct effects on the Ras-binding domain. Stabilization of the B-Raf(WT) KD was confirmed by hydrogen/deuterium exchange MS and molecular dynamics simulations. Our results are further supported by cellular assays in which we assessed cell viability and phosphorylation profiles in cells expressing B-Raf(WT) or B-Raf(V600E) in response to vemurafenib or sorafenib. Our data indicate that an overall stabilization of the B-Raf structure by specific inhibitors activates MAPK signaling and increases cell survival, helping to explain clinical treatment failure. We also applied our method to monitor conformational changes upon nucleotide binding of the pseudokinase KSR1, which holds high potential for inhibition in human diseases.

Iron-catalyzed trifluoromethylation of enamide.

Herein the first example of the iron(II)-catalyzed trifluoromethylation of enamide using mild and simple reaction conditions is reported. The method is cost-effective and uses the easy-to-handle Togni's reagent as the electrophilic CF3 source. This transformation is totally regioselective at the C3 position of enamides and exhibits broad substrate scope, good functional group tolerance and thus demonstrates its useful application in a late-stage fluorination strategy.

Exploring protein kinase conformation using swarm-enhanced sampling molecular dynamics.

Protein plasticity, while often linked to biological function, also provides opportunities for rational design of selective and potent inhibitors of their function. The application of computational methods to the prediction of concealed protein concavities is challenging, as the motions involved can be significant and occur over long time scales. Here we introduce the swarm-enhanced sampling molecular dynamics (sesMD) method as a tool to improve sampling of conformational landscapes. In this approach, a swarm of replica simulations interact cooperatively via a set of pairwise potentials incorporating attractive and repulsive components. We apply the sesMD approach to explore the conformations of the DFG motif in the protein p38α mitogen-activated protein kinase. In contrast to multiple MD simulations, sesMD trajectories sample a range of DFG conformations, some of which map onto existing crystal structures. Simulated structures intermediate between the DFG-in and DFG-out conformations are predicted to have druggable pockets of interest for structure-based ligand design.

[Chemical databases and virtual screening]. / Les chimiothèques et le criblage virtuel.

A prerequisite to any virtual screening is the definition of compound libraries to be screened. As we describe here, various sources are available. The selection of the proper library is usually project-dependent but at least as important as the screening method itself. This review details the main compound libraries that are available for virtual screening and guide the reader to the best possible selection according to its needs.