QSAR study for carcinogenicity in a large set of organic compounds

In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; ArgentinaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

kLog: A Language for Logical and Relational Learning with Kernels

We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe

ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction

Effective molecular representation learning is of great importance to facilitate molecular property prediction, which is a fundamental task for the drug and material industry. Recent advances in graph neural networks (GNNs) have shown great promise in applying GNNs for molecular representation learning. Moreover, a few recent studies have also demonstrated successful applications of self-supervised learning methods to pre-train the GNNs to overcome the problem of insufficient labeled molecules. However, existing GNNs and pre-training strategies usually treat molecules as topological graph data without fully utilizing the molecular geometry information. Whereas, the three-dimensional (3D) spatial structure of a molecule, a.k.a molecular geometry, is one of the most critical factors for determining molecular physical, chemical, and biological properties. To this end, we propose a novel Geometry Enhanced Molecular representation learning method (GEM) for Chemical Representation Learning (ChemRL). At first, we design a geometry-based GNN architecture that simultaneously models atoms, bonds, and bond angles in a molecule. To be specific, we devised double graphs for a molecule: The first one encodes the atom-bond relations; The second one encodes bond-angle relations. Moreover, on top of the devised GNN architecture, we propose several novel geometry-level self-supervised learning strategies to learn spatial knowledge by utilizing the local and global molecular 3D structures. We compare ChemRL-GEM with various state-of-the-art (SOTA) baselines on different molecular benchmarks and exhibit that ChemRL-GEM can significantly outperform all baselines in both regression and classification tasks. For example, the experimental results show an overall improvement of 8.8% on average compared to SOTA baselines on the regression tasks, demonstrating the superiority of the proposed method

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Gode -- Integrating Biochemical Knowledge Graph into Pre-training Molecule Graph Neural Network

The precise prediction of molecular properties holds paramount importance in facilitating the development of innovative treatments and comprehending the intricate interplay between chemicals and biological systems. In this study, we propose a novel approach that integrates graph representations of individual molecular structures with multi-domain information from biomedical knowledge graphs (KGs). Integrating information from both levels, we can pre-train a more extensive and robust representation for both molecule-level and KG-level prediction tasks with our novel self-supervision strategy. For performance evaluation, we fine-tune our pre-trained model on 11 challenging chemical property prediction tasks. Results from our framework demonstrate our fine-tuned models outperform existing state-of-the-art models.Comment: It's an ongoing work. We're exploring the ability of Gode on other task