Development, validation and application of in-silico methods to predict the macromolecular targets of small organic compounds

Computational methods to predict the macromolecular targets of small organic drugs and drug-like compounds play a key role in early drug discovery and drug repurposing efforts. These methods are developed by building predictive models that aim to learn the relationships between compounds and their targets in order to predict the bioactivity of the compounds. In this thesis, we analyzed the strategies used to validate target prediction approaches and how current strategies leave crucial questions about performance unanswered. Namely, how does an approach perform on a compound of interest, with its structural specificities, as opposed to the average query compound in the test data? We constructed and present new guidelines on validation strategies to address these short-comings. We then present the development and validation of two ligand-based target prediction approaches: a similarity-based approach and a binary relevance random forest (machine learning) based approach, which have a wide coverage of the target space. Importantly, we applied a new validation protocol to benchmark the performance of these approaches. The approaches were tested under three scenarios: a standard testing scenario with external data, a standard time-split scenario, and a close-to-real-world test scenario. We disaggregated the performance based on the distance of the testing data to the reference knowledge base, giving a more nuanced view of the performance of the approaches. We showed that, surprisingly, the similarity-based approach generally performed better than the machine learning based approach under all testing scenarios, while also having a target coverage which was twice as large. After validating two target prediction approaches, we present our work on a large-scale application of computational target prediction to curate optimized compound libraries. While screening large collections of compounds against biological targets is key to identifying new bioactivities, it is resource intensive and challenging. Small to medium-sized libraries, that have been optimized to have a higher chance of producing a true hit on an arbitrary target of interest are therefore valuable. We curated libraries of readily purchasable compounds by: i. utilizing property filters to ensure that the compounds have key physicochemical properties and are not overly reactive, ii. applying a similaritybased target prediction method, with a wide target scope, to predict the bioactivities of compounds, and iii. employing a genetic algorithm to select compounds for the library to maximize the biological diversity in the predicted bioactivities. These enriched small to medium-sized compound libraries provide valuable tool compounds to support early drug development and target identification efforts, and have been made available to the community. The distinctive contributions of this thesis include the development and benchmarking of two ligand-based target prediction approaches under novel validation scenarios, and the application of target prediction to enrich screening libraries with biologically diverse bioactive compounds. We hope that the insights presented in this thesis will help push data driven drug discovery forward.Doktorgradsavhandlin

Recommender systems in antiviral drug discovery

Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: Collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications

PSA 2018

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018

PSA 2018

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018

PSA 2018

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018

FIAS Scientific Report 2011

In the year 2010 the Frankfurt Institute for Advanced Studies has successfully continued to follow its agenda to pursue theoretical research in the natural sciences. As stipulated in its charter, FIAS closely collaborates with extramural research institutions, like the Max Planck Institute for Brain Research in Frankfurt and the GSI Helmholtz Center for Heavy Ion Research, Darmstadt and with research groups at the science departments of Goethe University. The institute also engages in the training of young researchers and the education of doctoral students. This Annual Report documents how these goals have been pursued in the year 2010. Notable events in the scientific life of the Institute will be presented, e.g., teaching activities in the framework of the Frankfurt International Graduate School for Science (FIGSS), colloquium schedules, conferences organized by FIAS, and a full bibliography of publications by authors affiliated with FIAS. The main part of the Report consists of short one-page summaries describing the scientific progress reached in individual research projects in the year 2010..

Translational Research in Cancer

Translational research in oncology benefits from an abundance of knowledge resulting from genome-scale studies concerning the molecular pathways involved in tumorigenesis. Translational oncology represents a bridge between basic research and clinical practice in cancer medicine. The vast majority of cancer cases are due to environmental risk factors. Many of these environmental factors are controllable lifestyle choices. Experimental cancer treatments are studied in clinical trials to compare the proposed treatment to the best existing treatment through translational research. The key features of the book include: 1) New screening for the development of radioprotectors: radioprotection and anti-cancer effect of β-Glucan (Enterococcus faecalis) 2) Translational perspective on hepatocellular carcinoma 3) Brachytherapy for endometrial cancer 4) Discovery of small molecule inhibitors for histone methyltransferases in cance

Generalised Bayesian matrix factorisation models

Factor analysis and related models for probabilistic matrix factorisation are of central importance to the unsupervised analysis of data, with a colourful history more than a century long. Probabilistic models for matrix factorisation allow us to explore the underlying structure in data, and have relevance in a vast number of application areas including collaborative filtering, source separation, missing data imputation, gene expression analysis, information retrieval, computational finance and computer vision, amongst others. This thesis develops generalisations of matrix factorisation models that advance our understanding and enhance the applicability of this important class of models. The generalisation of models for matrix factorisation focuses on three concerns: widening the applicability of latent variable models to the diverse types of data that are currently available; considering alternative structural forms in the underlying representations that are inferred; and including higher order data structures into the matrix factorisation framework. These three issues reflect the reality of modern data analysis and we develop new models that allow for a principled exploration and use of data in these settings. We place emphasis on Bayesian approaches to learning and the advantages that come with the Bayesian methodology. Our port of departure is a generalisation of latent variable models to members of the exponential family of distributions. This generalisation allows for the analysis of data that may be real-valued, binary, counts, non-negative or a heterogeneous set of these data types. The model unifies various existing models and constructs for unsupervised settings, the complementary framework to the generalised linear models in regression. Moving to structural considerations, we develop Bayesian methods for learning sparse latent representations. We define ideas of weakly and strongly sparse vectors and investigate the classes of prior distributions that give rise to these forms of sparsity, namely the scale-mixture of Gaussians and the spike-and-slab distribution. Based on these sparsity favouring priors, we develop and compare methods for sparse matrix factorisation and present the first comparison of these sparse learning approaches. As a second structural consideration, we develop models with the ability to generate correlated binary vectors. Moment-matching is used to allow binary data with specified correlation to be generated, based on dichotomisation of the Gaussian distribution. We then develop a novel and simple method for binary PCA based on Gaussian dichotomisation. The third generalisation considers the extension of matrix factorisation models to multi-dimensional arrays of data that are increasingly prevalent. We develop the first Bayesian model for non-negative tensor factorisation and explore the relationship between this model and the previously described models for matrix factorisation.Supported by a Commonwealth Scholarship awarded by the Commonwealth Scholarship and Fellowship Programme (CSFP) [Award number ZACS-2207-363] Supported by award from the National Research Foundation, South Africa (NRF) [Award number SFH2007072200001