Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Root-knot nematodes (RKN), from the Meloidogyne genus, have a worldwide distribution and cause severe economic damage to many life-sustaining crops. Because of their lack of specificity and danger to the environment, most chemical nematicides have been banned from use. Thus, there is a great need for new and safe compounds to control RKN. Such research involves identifying beforehand the nematode proteins essential to the invasion. Since G protein-coupled receptors GPCRs are the target of a large number of drugs, we have focused our research on the identification of putative nematode GPCRs such as those capable of controlling the movement of the parasite towards (or within) its host. A datamining procedure applied to the genome of Meloidogyne incognita allowed us to identify a GPCR, belonging to the neuropeptide GPCR family that can serve as a target to carry out a virtual screening campaign. We reconstructed a 3D model of this receptor by homology modeling and validated it through extensive molecular dynamics simulations. This model was used for large scale molecular dockings which produced a filtered limited set of putative antagonists for this GPCR. Preliminary experiments using these selected molecules allowed the identification of an active compound, namely C260-2124, from the ChemDiv provider, which can serve as a starting point for further investigations.

Objective:: Determining the factors associated with new-onset pre-diabetes and type 2 diabetes mellitus (T2D) is important for improving the current prevention strategies and for a better understanding of the disease. Design:: To study the factors (clinical, circulating protein and genetic) associated with new onset pre-diabetes and T2D in an initially healthy (without diabetes) populational familial cohort with a long follow-up (STANISLAS cohort). Methods:: A total of 1506 participants attended both the visit 1 and visit 4, separated by ≈20 years. Over 400 proteins, GWAS and genetic associations were studied using models adjusted for potential confounders. Both prospective (V1 to V4) and cross-sectional (V4) analyses were performed. Results:: People who developed pre-diabetes (n = 555) and/or T2D (n = 73) were older, had higher BMI, blood pressure, glucose, LDL cholesterol, and lower eGFR. After multivariable selection, PAPP-A (pappalysin-1) was the only circulating protein associated with the onset of both pre-diabetes and T2D with associations persisting at visit 4 (i.e. ≈20 years later). FGF-21 (fibroblast growth factor 21) was a strong prognosticator for incident T2D in the longitudinal analysis, but not in the cross-sectional analysis. The heritability of the circulating PAPP-A was estimated at 44%. In GWAS analysis, the SNP rs634737 was associated with PAPP-A both at V1 and V4. External replication also showed lower levels of PAPP-A in patients with T2D. Conclusions:: The risk of developing pre-diabetes and T2D increases with age and with features of the metabolic syndrome. Circulating PAPP-A, which has an important genetic component, was associated with both the development and presence of pre-diabetes and T2D.

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected—thanks to the molecular docking results—were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC ${}_{50}$ of 7.2 $\mathsf{\mu }$ M. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination. In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation. This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.