A5084617577- Computational Drug Discovery Methods
- Bioinformatics and Genomic Networks
- Biomedical Text Mining and Ontologies
- Pharmacogenetics and Drug Metabolism
- Cell Image Analysis Techniques
- Research Data Management Practices
- Biosimilars and Bioanalytical Methods
- Scientific Computing and Data Management
- Microbial Natural Products and Biosynthesis
- Genomics and Rare Diseases
- Receptor Mechanisms and Signaling
- Protein Structure and Dynamics
- Radiomics and Machine Learning in Medical Imaging
- Chemical Synthesis and Analysis
- Machine Learning in Bioinformatics
- Metabolomics and Mass Spectrometry Studies
- Innovative Microfluidic and Catalytic Techniques Innovation
- Data Visualization and Analytics
- Biological Research and Disease Studies
- Advanced Biosensing Techniques and Applications
- Inflammatory mediators and NSAID effects
- RNA modifications and cancer
- RNA and protein synthesis mechanisms
- Industrial Vision Systems and Defect Detection
- Protein Degradation and Inhibitors
University of Helsinki
2017-2025
Institute for Molecular Medicine Finland
2017-2025
Helsinki University Hospital
2024-2025
Biocenter Finland
2024-2025
Finland University
2017-2024
Technical University of Munich
2024
AstraZeneca (United Kingdom)
2024
Temple University
2021
Pasteur Institute of Iran
2021
Shahid Beheshti University
2021
Abstract Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used analyze pre-clinical drug combination datasets. Here, we report major updates for improved interpretation and annotation screening results. Unlike existing implementations, updated includes five main innovations. 1) We extend mathematical models higher-order data analysis implement dimension reduction techniques visualizing synergy...
Abstract Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent emergence of drug resistance. However, discovery synergistic effective combinations been a laborious often serendipitous process. In recent years, identification therapies accelerated due advances in high-throughput screening, but informatics approaches for systems-level data management analysis are needed. To contribute toward this goal, we created an open-access portal called...
Knowledge of the full target space bioactive substances, approved and investigational drugs as well chemical probes, provides important insights into therapeutic potential possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized heterogeneous assay types variability in endpoint measurements. To extract higher value from future compound target-profiling data, we implemented an open-data web platform, named Drug Target...
Abstract Despite decades of intensive search for compounds that modulate the activity particular protein targets, a large proportion human kinome remains as yet undrugged. Effective approaches are therefore required to map massive space unexplored compound–kinase interactions novel and potent activities. Here, we carry out crowdsourced benchmarking predictive algorithms kinase inhibitor potencies across multiple families tested on unpublished bioactivity data. We find top-performing...
The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study tumor-specific drug mechanism action. Here, this serves as the basis for DREAM Challenge assessing accuracy sensitivity computational algorithms de novo polypharmacology predictions. Dose-response perturbational 32 kinase inhibitors are provided 21 teams who blind...
Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization comprehensive mapping, reuse analysis of compound–target interaction profiles. End users can search, upload, edit, annotate export expert-curated further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology drug-repurposing applications, DTC version 2.0 includes...
Abstract The drug development process consumes 9–12 years and approximately one billion US dollars in costs. Due to the high finances time costs required by traditional discovery paradigm, repurposing old drugs treat cancer rare diseases is becoming popular. Computational approaches are mainly data-driven involve a systematic analysis of different data types leading formulation hypotheses. This study presents novel scoring algorithm based on chemical genomic repurpose for 669 from 22 groups,...
The main goals and challenges for the life science communities in Open Science framework are to increase reuse sustainability of data resources, software tools, workflows, especially large-scale data-driven research computational analyses. Here, we present key findings, procedures, effective measures recommendations generating establishing sustainable resources based on collaborative, cross-disciplinary work done within EOSC-Life (European Cloud Life Sciences) consortium. Bringing together...
Knowledge of the full target space drugs (or drug-like compounds) provides important insights into potential therapeutic use agents to modulate or avoid their various on- and off-targets in drug discovery precision medicine. However, there is a lack consolidated databases associated data exploration tools that allow for systematic profiling target-binding potencies both approved investigational using network-centric approach. We recently initiated community-driven platform, Drug Target...
Abstract RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines unique drug indication database with machine learning (ML) predictor to discover new drug-indication associations for approved as well investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications clinical trials across 25 categories, including neoplasms cardiovascular conditions. current version comprises 4314 compounds...
Aims/Purpose: To develop and validate a comprehensive web‐based interactive database of ocular diseases, including information of, for instance, associated genes, biomarkers, medications. Methods: Initially, thorough literature review was conducted to identify key diseases their genes biomarkers. Data then curated from multiple reputable sources, peer‐reviewed journals, clinical trial repositories drug regulatory agency databases. Advanced data integration techniques were employed link...
Aims/Purpose: To develop and validate a comprehensive web‐based interactive database of ocular diseases, including information of, for instance, associated genes, biomarkers, medications. Methods: Initially, thorough literature review was conducted to identify key diseases their genes biomarkers. Data then curated from multiple reputable sources, peer‐reviewed journals, clinical trial repositories drug regulatory agency databases. Advanced data integration techniques were employed link...
Reliable and reproducible drug screening experiments are essential for discovery personalized medicine. Here, we demonstrate how systematic experimental errors negatively impact reproducibility, that conventional quality control (QC) methods based on plate controls fail to detect these errors. To address this limitation, developed a control-independent QC approach using normalized residual fit error (NRFE) identify in experiments. Comprehensive analysis of >100,000 duplicate measurements...
Artificial intelligence (AI) and machine learning (ML) techniques play an increasingly crucial role in the field of drug repurposing. As number computational tools grows, it is essential to not only understand carefully select method itself, but also consider input data used for building predictive models. This review aims take a dive into current methods that leverage AI ML drive accelerate compound target selection, addition addressing existing challenges providing perspectives. While...
Abstract Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used analyze pre-clinical drug combination datasets. Here, we report major updates for improved interpretation and annotation screening results. Unlike existing implementations, updated includes five main innovations. (1) We extend mathematical models higher-order data analysis implement dimension reduction techniques visualizing synergy...
Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of mechanisms, manually curated by large databases, such as ChEMBL, BindingDB, DrugBank DrugTargetCommons. However, the number articles likely constitutes only a fraction all that contain experimentally determined DTIs. Finding extracting experimental information is challenging task, there pressing need systematic approaches to assist curation To this end, we applied Bidirectional Encoder Representations from...
Abstract Functional precision medicine (fPM) offers an exciting, simplified approach to finding the right applications for existing molecules and enhancing therapeutic potential. Integrative robust tools ensuring high accuracy reliability of results are critical. In response this need, we previously developed Breeze, a drug screening data analysis pipeline, designed facilitate quality control, dose-response curve fitting, visualization in user-friendly manner. Here, describe latest version...
Abstract Motivation Peptide therapeutics hinge on the precise interaction between a tailored peptide and its designated receptor while mitigating interactions with alternate receptors is equally indispensable. Existing methods primarily estimate binding score protein pairs. However, for specific without corresponding protein, it challenging to identify proteins could bind due sheer number of potential candidates. Results We propose transformers-based embedding scheme in this study that can...
Artificial Intelligence (AI) and Machine Learning (ML) techniques play an increasingly crucial role in the field of drug repurposing. As number computational tools grows, it is essential to not only understand carefully select method itself, but also consider input data used for building predictive models. This review aims take a dive into current methods that leverage AI ML drive accelerate compound target selection, addition addressing existing challenges providing perspectives. While...
Abstract Pharmacogenomics, the study of how an individual's genetic makeup influences their response to medications, is a rapidly evolving field with significant implications for personalized medicine. As researchers and healthcare professionals face challenges in exploring intricate relationships between profiles therapeutic outcomes, demand effective user-friendly tools access analyze data related drug responses continues grow. To address these challenges, we have developed PGxDB,...
Abstract We conduct a cartography of rhodopsin-like non-olfactory G protein-coupled receptors in the Ensembl database. The most recent genomic data (releases 90–92, 90 vertebrate genomes) are analyzed through online interface and mapped on phylogenetic guide trees that were constructed based set ~14.000 amino acid sequences. This snapshot suggest genomes to harbour 142 clades GPCRs without human orthologues. Among those, 69 have not our knowledge been mentioned or studied previously...