Pharmaceutical Bioinformatics

Description

Accelerating drug discovery and chemical safety using artificial intelligence, automation and intelligent design of experiments.

The Pharmaceutical Bioinformatics research group, headed by Professor Ola Spjuth, focuses on data-driven drug discovery and chemical safety. We are an interdisciplinary team of researchers with a vision to develop autonomous biological laboratories where data drives decisions on what experiments should be performednext, and where automation and AI modeling are core methodologies. We combine in silico and in vitro experiments at the cellular level, and have access to a robotized high-content imaging lab connected to a modern IT-infrastructure to manage and analyze large-scale data

Examples of applications include elucidating mechanism-of-action and safety for novel chemical compounds and screening for new and repurposed drugs. Of particular interest to us is to develop and use autonomous systems integrating laboratory equipment with AI for iterative discovery to tackle previously infeasible problems. Here, ongoing projects include searching for optimal individualized drug combinations in precision cancer medicine, and also identifying synergistic combinations of environmental chemicals. We are involved in several national and international consortia and have a tight connection to the pharmaceutical industry, Uppsala University Hospital, and Science for Life Laboratory.

Publications

Data management of scientific applications in a reinforcement learning-based hierarchical storage system

Zhang, Tianru; Gupta, Ankit; Francisco Rodríguez, María Andreína; Spjuth, Ola et al.

Part of Expert systems with applications, 2024
- DOI for Data management of scientific applications in a reinforcement learning-based hierarchical storage system
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From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability

Seal, Srijit; Carreras-Puigvert, Jordi; Singh, Shantanu; Carpenter, Anne E. et al.

Part of Molecular Biology of the Cell, 2024
- DOI for From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability
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Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data

Seal, Srijit; Yang, Hongbin; Trapotsi, Maria-Anna; Singh, Satvik et al.

Part of Journal of Cheminformatics, 2023
- DOI for Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data
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In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Spjuth, Ola

Part of ATLA (Alternatives to Laboratory Animals), p. 39-54, 2023
- DOI for In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods
Development of new approach methods for the identification and characterization of endocrine metabolic disruptors: a PARC project

Braeuning, Albert; Balaguer, Patrick; Bourguet, William; Carreras-Puigvert, Jordi et al.

Part of Frontiers in Toxicology, 2023
- DOI for Development of new approach methods for the identification and characterization of endocrine metabolic disruptors: a PARC project
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Evaluating the utility of brightfield image data for mechanism of action prediction

Harrison, Philip John; Gupta, Ankit; Rietdijk, Jonne; Wieslander, Håkan et al.

Part of PloS Computational Biology, 2023
- DOI for Evaluating the utility of brightfield image data for mechanism of action prediction
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Designing microplate layouts using artificial intelligence

Francisco Rodríguez, María Andreína; Carreras-Puigvert, Jordi; Spjuth, Ola

Part of Artificial Intelligence in the Life Sciences, 2023
- DOI for Designing microplate layouts using artificial intelligence
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Disease phenotype prediction in multiple sclerosis

Herman, Stephanie; Arvidsson McShane, Staffan; Zhukovsky, Christina; Emami, Payam et al.

Part of iScience, 2023
- DOI for Disease phenotype prediction in multiple sclerosis
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Predicting protein network topology clusters from chemical structure using deep learning

Sreenivasan, Akshai P.; Harrison, Philip J; Schaal, Wesley; Matuszewski, Damian J. et al.

Part of Journal of Cheminformatics, 2022
- DOI for Predicting protein network topology clusters from chemical structure using deep learning
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In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Spjuth, Ola

Part of Xenobiotica, p. 113-118, 2022
- DOI for In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology
From biomedical cloud platforms to microservices: next steps in FAIR data and analysis

Sheffield, Nathan C.; Bonazzi, Vivien R.; Bourne, Philip E.; Burdett, Tony et al.

Part of Scientific Data, 2022
- DOI for From biomedical cloud platforms to microservices: next steps in FAIR data and analysis
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Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening

Schaal, Wesley; Ameur, Adam; Olsson-Strömberg, Ulla; Hermansson, Monica et al.

Part of Cancer Informatics, p. 1-8, 2022
- DOI for Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening
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Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

Olsson, Henrik; Kartasalo, Kimmo; Mulliqi, Nita; Capuccini, Marco et al.

Part of Nature Communications, 2022
- DOI for Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction
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A method for Boolean analysis of protein interactions at a molecular level

Raykova, Doroteya; Kermpatsou, Despoina; Malmqvist, Tony; Harrison, Philip J et al.

Part of Nature Communications, 2022
- DOI for A method for Boolean analysis of protein interactions at a molecular level
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Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

Rietdijk, Jonne; Aggarwal, Tanya; Georgiev, Polina; Lapins, Maris et al.

Part of Science of the Total Environment, 2022
- DOI for Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects
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In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Spjuth, Ola

Part of Journal of Pharmaceutical Sciences, p. 2614-2619, 2022
- DOI for In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology
The Impact of Reference Data Selection for the Prediction Accuracy of Intrinsic Hepatic Metabolic Clearance

Fagerholm, Urban; Spjuth, Ola; Hellberg, Sven

Part of Journal of Pharmaceutical Sciences, p. 2645-2649, 2022
- DOI for The Impact of Reference Data Selection for the Prediction Accuracy of Intrinsic Hepatic Metabolic Clearance
An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

Ouyang, Wei; Bowman, Richard W; Wang, Haoran; Bumke, Kaspar E et al.

Part of Advanced biology, 2022
- DOI for An Open-Source Modular Framework for Automated Pipetting and Imaging Applications
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Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

Seal, Srijit; Carreras-Puigvert, Jordi; Trapotsi, Maria-Anna; Yang, Hongbin et al.

Part of Communications Biology, 2022
- DOI for Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection
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SimVec: predicting polypharmacy side effects for new drugs

Lukashina, Nina; Kartysheva, Elena; Spjuth, Ola; Virko, Elizaveta et al.

Part of Journal of Cheminformatics, 2022
- DOI for SimVec: predicting polypharmacy side effects for new drugs
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Synergy Conformal Prediction

Gauraha, Niharika; Spjuth, Ola

Part of Proceedings of Machine Learning Research, p. 91-119, 2021
Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images

Wieslander, Håkan; Harrison, Philip J.; Skogberg, Gabriel; Jackson, Sonya et al.

Part of IEEE journal of biomedical and health informatics, p. 371-380, 2021
- DOI for Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images
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Approaches for containerized scientific workflows in cloud environments with applications in life science

Spjuth, Ola; Capuccini, Marco; Carone, Matteo; Larsson, Anders et al.

Part of F1000 Research, p. 513-513, 2021
- DOI for Approaches for containerized scientific workflows in cloud environments with applications in life science
ELIXIR and Toxicology: a community in development

Martens, Marvin; Stierum, Rob; Schymanski, Emma L.; Evelo, Chris T. et al.

Part of F1000 Research, p. 1129-1129, 2021
- DOI for ELIXIR and Toxicology: a community in development
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Assessing the calibration in toxicological in vitro models with conformal prediction

Morger, Andrea; Svensson, Fredrik; Arvidsson McShane, Staffan; Gauraha, Niharika et al.

Part of Journal of Cheminformatics, 2021
- DOI for Assessing the calibration in toxicological in vitro models with conformal prediction
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Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

Norinder, Ulf; Spjuth, Ola; Svensson, Fredrik

Part of Journal of Cheminformatics, 2021
- DOI for Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning
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The machine learning life cycle and the cloud: implications for drug discovery.

Spjuth, Ola; Frid, Jens; Hellander, Andreas

Part of Expert Opinion on Drug Discovery, p. 1071-1079, 2021
- DOI for The machine learning life cycle and the cloud: implications for drug discovery.
scConnect: a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data

Jakobsson, Jon E. T.; Spjuth, Ola; Lagerström, Malin C.

Part of Bioinformatics, p. 3501-3508, 2021
- DOI for scConnect: a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data
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Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

Blamey, Ben; Toor, Salman; Dahlö, Martin; Wieslander, Håkan et al.

Part of GigaScience, p. 1-14, 2021
- DOI for Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
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Machine Learning Strategies When Transitioning between Biological Assays

Arvidsson McShane, Staffan; Ahlberg, Ernst; Noeske, Tobias; Spjuth, Ola

Part of Journal of Chemical Information and Modeling, p. 3722-3733, 2021
- DOI for Machine Learning Strategies When Transitioning between Biological Assays
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In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models

Fagerholm, Urban; Hellberg, Sven; Alvarsson, Jonathan; Arvidsson McShane, Staffan et al.

Part of Xenobiotica, p. 1366-1371, 2021
- DOI for In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models
Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Fagerholm, Urban; Spjuth, Ola; Hellberg, Sven

Part of Xenobiotica, p. 1095-1100, 2021
- DOI for Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma
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Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival

Lukashina, Nina; Williams, Michael J.; Kartysheva, Elena; Virko, Elizaveta et al.

Part of International Journal of Molecular Sciences, 2021
- DOI for Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival
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Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology

Fagerholm, Urban; Hellberg, Sven; Spjuth, Ola

Part of Molecules, 2021
- DOI for Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology
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Metabolomics: The Stethoscope for the Twenty-First Century

Ashrafian, Hutan; Sounderajah, Viknesh; Glen, Robert; Ebbels, Timothy et al.

Part of Medical principles and practice, p. 301-310, 2021
- DOI for Metabolomics: The Stethoscope for the Twenty-First Century
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Deep-learning models for lipid nanoparticle-based drug delivery

Harrison, Philip J.; Wieslander, Håkan; Sabirsh, Alan; Karlsson, Johan et al.

Part of Nanomedicine, p. 1097-1110, 2021
- DOI for Deep-learning models for lipid nanoparticle-based drug delivery
A phenomics approach for antiviral drug discovery

Rietdijk, Jonne; Tampere, Marianna; Pettke, Aleksandra; Georgiev, Polina et al.

Part of BMC Biology, 2021
- DOI for A phenomics approach for antiviral drug discovery
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Predicting target profiles with confidence as a service using docking scores

Ahmed, Laeeq; Alogheli, Hiba; Arvidsson Mc Shane, Staffan; Alvarsson, Jonathan et al.

Part of Journal of Cheminformatics, 2020
- DOI for Predicting target profiles with confidence as a service using docking scores
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Using Predicted Bioactivity Profiles to Improve Predictive Modeling

Norinder, Ulf; Spjuth, Ola; Svensson, Fredrik

Part of Journal of Chemical Information and Modeling, p. 2830-2837, 2020
- DOI for Using Predicted Bioactivity Profiles to Improve Predictive Modeling
MaRe: Processing Big Data with application containers on Apache Spark

Capuccini, Marco; Dahlö, Martin; Toor, Salman; Spjuth, Ola

Part of GigaScience, 2020
- DOI for MaRe: Processing Big Data with application containers on Apache Spark
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Container-based bioinformatics with Pachyderm

Novella, Jon Ander; Emami Khoonsari, Payam; Herman, Stephanie; Whitenack, Daniel et al.

Part of Bioinformatics, p. 839-846, 2019
- DOI for Container-based bioinformatics with Pachyderm
SciPipe - Turning Scientific Workflows into Computer Programs

Lampa, Samuel; Dahlö, Martin; Alvarsson, Jonathan; Spjuth, Ola

Part of Computing in science & engineering (Print), p. 109-113, 2019
- DOI for SciPipe - Turning Scientific Workflows into Computer Programs
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On-demand virtual research environments using microservices

Capuccini, Marco; Larsson, Anders; Carone, Matteo; Novella, Jon Ander et al.

Part of PeerJ Computer Science, 2019
- DOI for On-demand virtual research environments using microservices
Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis

Herman, Stephanie; Åkerfeldt, Torbjörn; Spjuth, Ola; Burman, Joachim et al.

Part of Cells, 2019
- DOI for Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing-Remitting Multiple Sclerosis
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Interoperable and scalable data analysis with microservices: Applications in metabolomics

Emami Khoonsari, Payam; Moreno, Pablo; Bergmann, Sven; Burman, Joachim et al.

Part of Bioinformatics, p. 3752-3760, 2019
- DOI for Interoperable and scalable data analysis with microservices: Applications in metabolomics
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Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects

Herman, Stephanie; Niemelä, Valter; Emami Khoonsari, Payam; Sundblom, Jimmy et al.

Part of Scientific Reports, 2019
- DOI for Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects
- Download full text (pdf) of Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington's disease subjects
PhenoMeNal: Processing and analysis of metabolomics data in the cloud

Peters, Kristian; Bradbury, James; Bergmann, Sven; Capuccini, Marco et al.

Part of GigaScience, 2019
- DOI for PhenoMeNal: Processing and analysis of metabolomics data in the cloud
SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines

Lampa, Samuel; Dahlö, Martin; Alvarsson, Jonathan; Spjuth, Ola

Part of GigaScience, 2019
- DOI for SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines
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Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes

Kensert, Alexander; Harrison, Philip J; Spjuth, Ola

Part of SLAS discovery : advancing life sciences R & D, p. 466-475, 2019
- DOI for Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
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Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty

Svensson, Fredrik; Aniceto, Natalia; Norinder, Ulf; Cortes-Ciriano, Isidro et al.

Part of Journal of Chemical Information and Modeling, p. 1132-1140, 2018
- DOI for Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty