A novel interaction fingerprint derived from per atom score contributions

Protein ligand interaction fingerprints are a powerful approach for the analysis and assessment of docking poses to improve docking performance in virtual screening. In this study, a novel interaction fingerprint approach (PADIF, protein per atom score contributions derived interaction fingerprint) is presented which was specifically designed for utilising the GOLD scoring functions’ atom contributions together with a specific scoring scheme. This allows the incorporation of known protein–ligand complex structures for a target-specific scoring. Unlike many other methods, this approach uses weighting factors reflecting the relative frequency of a specific interaction in the references and penalizes destabilizing interactions. In addition, and for the first time, an exhaustive validation study was performed that assesses the performance of PADIF and two other interaction fingerprints in virtual screening. Here, PADIF shows superior results, and some rules of thumb for a successful use of interaction fingerprints could be identified

Betrachtung der Ähnlichkeit von niedermolekularen Verbindungen unter Berücksichtigung der biologischen Aktivität

Im Rahmen dieser Arbeit sollte die Ähnlichkeit von niedermolekularen Verbindungen unter Berücksichtigung der biologischen Aktivität betrachtet werden. Dazu sollte zunächst eine Datenbank solcher Verbindungen generiert werden, die einen weiten chemischen Raum abdeckt und deren Verbindungen größtenteils zugleich synthetisch zugänglich sind. Diese sollte als Ideengenerator in zukünftigen Wirkstoffdesignprojekten verwendet werden können, indem die in den nachfolgenden Analysen zur Ähnlichkeit entwickelten Methoden auf sie angewendet werden. Anschließend sollten mehrere Ebenen der Ähnlichkeit von niedermolekularen Verbindungen untersucht werden: (i) Atomtypengruppen und pharmakophore Eigenschaften, (ii) Scaffolds (Grundgerüste) sowie (iii) Bioisostere und Shape (Form). Dabei sollten zwei unterschiedliche Methoden entwickelt werden. Die erste Methode sollte die gewonnenen Erkenntnisse aus den Ähnlichkeitsanalysen in Graph-basierte Such- oder Clustering-Algorithmen zur Erfassung der Bioähnlichkeit von niedermolekularen Verbindungen integrieren. Die zweite Methode sollte eine Erfassung der biologischen Zielstruktur (Protein) in der Betrachtung der Bioähnlichkeit von niedermolekularen Verbindungen erlauben. Zur Validierung sollten die Vorhersagen dieser Methode biochemisch verifiziert werden. Die entwickelten Methoden sollen eine Einbeziehung der biologischen Aktivität in die Ähnlichkeitsbetrachtung von niedermolekularen Verbindungen erlauben und somit zusammen mit der generierten Datenbank die Wirkstoffentwicklung fördern

Discovery of an Unexpected Similarity in Ligand Binding Between BRD4 and PPARγ

Knowledge about interrelationships between different proteins is crucial in fundamental research for the elucidation of protein networks and pathways. Furthermore, it is especially critical in chemical biology to identify further key regulators of a disease and to take advantage of polypharmacology effects. A comprehensive scaffold-based analysis uncovered an unexpected relationship between bromodomain-containing protein 4 (BRD4) and peroxisome-proliferator activated receptor gamma (PPARγ). They are both important drug targets for cancer therapy and many more important diseases. Both proteins share binding site similarities near a common hydrophobic subpocket which should allow the design of a polypharmacology-based ligand targeting both proteins. Such a dual-BRD4-PPARγ-modulator could show synergistic effects with a higher efficacy or delayed resistance development in, for example, cancer therapy. Thereon, a complex structure of sulfasalazine was obtained that involves two bromodomains and could be a potential starting point for the design of a bivalent BRD4 inhibitor

A novel interaction fingerprint derived from per atom score contributions: exhaustive evaluation of interaction fingerprint performance in docking based virtual screening

Abstract Protein ligand interaction fingerprints are a powerful approach for the analysis and assessment of docking poses to improve docking performance in virtual screening. In this study, a novel interaction fingerprint approach (PADIF, protein per atom score contributions derived interaction fingerprint) is presented which was specifically designed for utilising the GOLD scoring functions’ atom contributions together with a specific scoring scheme. This allows the incorporation of known protein–ligand complex structures for a target-specific scoring. Unlike many other methods, this approach uses weighting factors reflecting the relative frequency of a specific interaction in the references and penalizes destabilizing interactions. In addition, and for the first time, an exhaustive validation study was performed that assesses the performance of PADIF and two other interaction fingerprints in virtual screening. Here, PADIF shows superior results, and some rules of thumb for a successful use of interaction fingerprints could be identified

Multi-task ADME/PK Prediction at Industrial Scale: Leveraging Large and Diverse Experimental Datasets

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 models, 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

Scaffold Hunter: a comprehensive visual analytics framework for drug discovery

Abstract The era of big data is influencing the way how rational drug discovery and the development of bioactive molecules is performed and versatile tools are needed to assist in molecular design workflows. Scaffold Hunter is a flexible visual analytics framework for the analysis of chemical compound data and combines techniques from several fields such as data mining and information visualization. The framework allows analyzing high-dimensional chemical compound data in an interactive fashion, combining intuitive visualizations with automated analysis methods including versatile clustering methods. Originally designed to analyze the scaffold tree, Scaffold Hunter is continuously revised and extended. We describe recent extensions that significantly increase the applicability for a variety of tasks

Splitting chemical structure data sets for federated privacy-preserving machine learning.

With the increase in applications of machine learning methods in drug design and related fields, the challenge of designing sound test sets becomes more and more prominent. The goal of this challenge is to have a realistic split of chemical structures (compounds) between training, validation and test set such that the performance on the test set is meaningful to infer the performance in a prospective application. This challenge is by its own very interesting and relevant, but is even more complex in a federated machine learning approach where multiple partners jointly train a model under privacy-preserving conditions where chemical structures must not be shared between the different participating parties. In this work we discuss three methods which provide a splitting of a data set and are applicable in a federated privacy-preserving setting, namely: a. locality-sensitive hashing (LSH), b. sphere exclusion clustering, c. scaffold-based binning (scaffold network). For evaluation of these splitting methods we consider the following quality criteria (compared to random splitting): bias in prediction performance, classification label and data imbalance, similarity distance between the test and training set compounds. The main findings of the paper are a. both sphere exclusion clustering and scaffold-based binning result in high quality splitting of the data sets, b. in terms of compute costs sphere exclusion clustering is very expensive in the case of federated privacy-preserving setting

Don’t overweight weights: Evaluation of weighting strategies for multi-task bioactivity classification models

Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow priva-cy-preserving usage of large amount of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning

Splitting chemical structure data sets for federated privacy-preserving machine learning

With the increase in applications of machine learning methods in drug design and related fields, the challenge of designing sound test sets becomes more and more prominent. The goal of this challenge is to have a realistic split of chemical structures (compounds) between training, validation and test set such that the performance on the test set is meaningful to infer the performance in a prospective application. This challenge is by its own very interesting and relevant,but is even more complex in a federated machine learning approach where multiple partners jointly train a model under privacy-preserving conditions where chemical structures must not be shared between the different participating parties in the federated learning. In this work we discuss three methods which provide a splitting of the data set and are applicable in a federated privacy-preserving setting, namely: a. locality-sensitive hashing (LSH), b. sphere exclusion clustering, c. scaffold-based binning (scaffold network). For evaluation of these splitting methods we consider the following quality criteria: bias in prediction performance, label and data imbalance, distance of the test set compounds to the training set and compare them to a random splitting. The main findings of the paper are a. both sphere exclusion clustering and scaffold-based binning result in high quality splitting of the data sets, b. in terms of compute costs sphere exclusion clustering is very expensive in the case of federated privacy-preserving setting