Molecular Informatics最新文献

The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.

The insulin superfamily proteins (ISPs), in particular, insulin, IGFs and relaxin proteins are key modulators of animal physiology. They are known to have evolved from the same ancestral gene and have diverged into proteins with varied sequences and distinct functions, but maintain a similar structural architecture stabilized by highly conserved disulphide bridges. The recent surge of sequence data and the structures of these proteins prompted a need for a comprehensive analysis, which connects the evolution of these sequences (427 sequences) in the light of available functional and structural information including representative complex structures of ISPs with their cognate receptors. This study reveals (a) unusually high sequence conservation of IGFs (>90 % conservation in 184 sequences) and provides a possible structure-based rationale for such high sequence conservation; (b) provides an updated definition of the receptor-binding signature motif of the functionally diverse relaxin family members (c) provides a probable non-canonical C-peptide cleavage site in a few insulin sequences. The high conservation of IGFs appears to represent a classic case of resistance to sequence diversity exerted by physiologically important interactions with multiple partners. We also propose a probable mechanism for C-peptide cleavage in a few distinct insulin sequences and redefine the receptor-binding signature motif of the relaxin family. Lastly, we provide a basis for minimally modified insulin mutants with potential therapeutic application, inspired by concomitant changes observed in other insulin superfamily protein members supported by molecular dynamics simulation.

ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME and animal PK endpoints trained on in-house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time-splits, we found a clear benefit in performance of multi-task graph-based neural network models over single-task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi-task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models.

The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built-in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained.

Aminoglycosides are crucial antibiotics facing challenges from bacterial resistance. This study addresses the importance of aminoglycoside modifying enzymes in the context of escalating resistance. Drawing upon over two decades of structural data in the Protein Data Bank, we focused on two key antibiotics, neomycin B and kanamycin A, to explore how the aminoglycoside structure is exploited by this family of enzymes. A systematic comparison across diverse enzymes and the RNA A-site target identified common characteristics in the recognition mode, while assessing the adaptability of neomycin B and kanamycin A in various environments.

In this article we try different algorithms, namely Nested Monte Carlo Search and Greedy Best First Search, on AstraZeneca's open source retrosynthetic tool : AiZynthFinder. We compare these algorithms to AiZynthFinder's base Monte Carlo Tree Search on a benchmark selected from the PubChem database and by Bayer's chemists. We show that both Nested Monte Carlo Search and Greedy Best First Search outperform AstraZeneca's Monte Carlo Tree Search, with a slight advantage for Nested Monte Carlo Search while experimenting on a playout heuristic. We also show how the search algorithms are bounded by the quality of the policy network, in order to improve our results the next step is to improve the policy network.