Integrative relational machine-learning for understanding drug side-effect profiles

International audienceBackgroundDrug 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.ResultsIn 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.ConclusionsSide 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

Synthese de la (+) anticapsine et d'analogues structuraux : evaluation de leurs proprietes biologiques

CNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Cluster Induced fit in liver X receptor beta: a molecular dynamics-based investigation

International audienceLigand induced fit phenomenon occurring at the ligand binding domain of the liver X receptor beta (LXR) was investigated by means of molecular dynamics. Reliability of a 4-ns trajectory was tested from two distinct LXR crystal complexes 1PQ6B/GW and 1PQ9B/T09 characterized by an open and a closed state of the pocket, respectively. Crossed complexes 1PQ6B/T09 and 1PQ9B/GW were then submitted to the same molecular dynamic conditions, which were able to recover LXR conformations similar to the original crystallography data. Analysis of open to closed and closed to open conformational transitions pointed out the dynamic role of critical residues lining the ligand binding pocket involved in the local remodeling upon ligand binding (e.g., Phe271, Phe329, Phe340, Arg319, Glu281). Altogether, the present study indicates that the molecular dynamic protocol is a consistent approach for managing LXR-related induced fit process. This protocol could therefore be used for refining ligand docking solutions of a structure-based design strategy

GES Polypharmacology Fingerprints: A Novel Approach for Drug Repositioning

International audienc

Three dimensional finite element simulation of ring rolling

International audienceThe ring rolling process is particularly demanding for FEM softwares: large pieces are deformed locally, requiring large meshes, and 10 to 20 rotations of the ring are imposed, requiring thousands of increments. An ALE technique with splitting has been built to maintain a fine mesh in the bites and a coarser one outside. To avoid large volume changes associated with the rotational movement, B-spline smoothing of the outer surface was necessary before the UL-convected nodes are moved back into the bite area. Furthermore, the one-step forward Euler time integration was replaced by a second-order, 2-step Runge-Kutta scheme. This ALE model has been implemented within the Forge3(R) implicit 3D-FEM software : (V,p) formulation, parallel version through domain partitioning, 4-node tetrahedra mini-elements, contact penalty technique, Newton-Raphson's method with line search; the linear systems of equations are solved by an iterative, Minimal Residual method with an Incomplete Cholesky factorization as a preconditioner. Force- and torque-controlled tools have been introduced. The equations and solution methods are presented with some detail, together with an application

Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments

International audienceSince 3D molecular shape is an important determinant of biological activity, designing accurate 3D molecular representations is still of high interest. Several chemoinformatic approaches have been developed to try to describe accurate molecular shapes. Here, we present a novel 3D molecular description, namely harmonic pharma chemistry coefficient (HPCC), combining a ligand-centric pharmacophoric description projected onto a spherical harmonic based shape of a ligand. The performance of HPCC was evaluated by comparison to the standard ROCS software in a ligand-based virtual screening (VS) approach using the publicly available directory of useful decoys (DUD) data set comprising over 100,000 compounds distributed across 40 protein targets. Our results were analyzed using commonly reported statistics such as the area under the curve (AUC) and normalized sum of logarithms of ranks (NSLR) metrics. Overall, our HPCC 3D method is globally as efficient as the state-of-the-art ROCS software in terms of enrichment and slightly better for more than half of the DUD targets. Since it is largely admitted that VS results depend strongly on the nature of the protein families, we believe that the present HPCC solution is of interest over the current ligand-based VS methods

A KDD Approach for Designing Filtering Strategies to Improve Virtual Screening

International audienceVirtual screening has become an essential step in the early drug discovery process. Generally speaking, it consists in using computational techniques for selecting compounds from chemical libraries in order to identify drug-like molecules acting on a biological target of therapeutic interest. In the present study we consider virtual screening as a particular form of the KDD (Knowledge Discovery from Databases) approach. The knowledge to be discovered concerns the way a compound can be considered as a consistent ligand for a given target. The data from which this knowledge has to be discovered derive from diverse sources such as chemical, structural, and biological data related to ligands and their cognate targets. More precisely, we aim to extract filters from chemical libraries and protein-ligand interactions. In this context, the three basic steps of a KDD process have to be implemented. Firstly, a model-driven data integration step is applied to appropriate heterogeneous data found in public databases. This facilitates subsequent extraction of various datasets for mining. In particular and for specific ligand descriptors, it allows transforming a multiple-instance problem into a single-instance one. In a second step, mining algorithms are applied to the datasets and finally the most accurate knowledge units are eventually proposed as new filters. We report here this KDD approach and the experimental results we obtained with a set of ligands of the hormone receptor LXR

Effets combinés de l'apport d'acide caproïque et de la concentration en protéines sur l'utilisation de laits artificiels par l'agneau préruminant. 1 - Utilisation digestive des principaux constituants du régime

National audienceLes effets de la nature des lipides (suif-coprah, « SC », ou suif-coprah tricaproïne, « SCT »), de la teneur en protéine (moyenne, « M », c.à.d. 24 p. 100 de la matière sèche, « MS », ou haute, « H », c.à.d. 31 p. 100 de MS) ou de la quantité de tricaproïne utilisée (4,5 ou 11,4 p. 100 de MS) sur l’utilisation digestive de laits de remplacements ont été comparés au cours de 3 essais successifs mettant en jeu 56 agneaux préruminants. La digestibilité a été mesurée à 2 et 5 semaines (essai 1, 16 agneaux limousins), à 1, 2 et 3 semaines (essai 2, 24 agneaux limousins) et à 2 et 4 semaines (essai 3, 16 agneaux Ile-de-France). Dans le cas des laits « SC », la digestibilité de MS, E (énergie) et N (azote) augmente de façon linéaire (P < 0,005) entre la première semaine (CUDa de 95,5, 94,6 et 95,1 p. 100 respectivement) et la troisième semaine (CUDa de 98,0, 97,6 et 97,0 p. 700 respectivement). Pour ce type de laits, la digestibilité des 3 constituants s’accroît avec la teneur en protéines (P < 0,05) chez les animaux les plus jeunes (1 et 2 semaines) de 95,3, 94,2 et 95,0 p. 100 à 96,8, 97,1 et 96,3 p. 100 respectivement pour les laits « SCM » ou « SCH ». Dans le cas des laits « SCT », la digestibilité des 3 constituants n’est pas influencée par l’âge des animaux ou la teneur en protéines des laits ; en comparaison des valeurs observées dans le cas des laits « SC », elle est accrue (P < 0,05) chez les animaux les plus jeunes (1 et 2 semaines) de 96,1, 95,6 et 95,5 p. 100 à 98,4, 98,3 et 97,0 p. 100 respectivement pour MS, E ou N. Dans le cas des laits « SCM » et chez les animaux de 1 semaine, la digestibilité des 3 constituants est corrélée positivement avec le poids de naissance des agneaux (r = 0,70) et inversement avec leur vitesse de croissance dans le cas de MS et de E (r = - 0,86). A tous les autres âges ou pour tous les autres régimes, aucune relation ne relie la digestibilité de MS, E ou N au poids de naissance ou à la vitesse de croissance des animaux