A collection of software written and contributed to by SABS-R3 students.

If you want to contribute, please read this.

• SABS-R3 Software Projects
• Libraries
• Applications
• Demos / Tutorials
• Publications
• Other

The Drug Discovery Game - An interactive web-application designed for medicinal chemists to learn about the drug discovery and design process. The aim of the game is to design a compound which inhibits the protein MMP-12 by running various tests and assays.

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• PINTS — a framework for optimisation and Bayesian inference on ODE models of noisy time-series, such as arise in electrochemistry and cardiac electrophysiology.

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• — Chi is a framework for pharmacokinetic and pharmacodynamic (PKPD) modelling, including simulation of treatment responses and the inference of model parameters from population data.

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• ImmuneBuilder — A set of deep learning models to predict the structure of antibodies, nanobodies and TCRs.

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• ABlooper — Tool for modelling the CDR loops in antibodies.

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• branchpro — Tool for modelling branching processes to estimate the time-dependent reproduction number of a disease with imported cases.

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• seirmo — Tool for modelling the outbreak of an infectious disease with the SEIR model.

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• epiabm — Epidemiological agent-based modelling packages in both Python and C++.

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• epimodels — A collection of multiple epidemiological models used in the modelling of cases observed during the COVID-19 pandemic.

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• Paragraph — Antibody paratope prediction using graph neural networks with minimal feature vectors.

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• AbLang — AbLang: A language model for antibodies.

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• KA-Search — KA-Search: Rapid and exhaustive sequence identity search of known antibodies.

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• fragment-ranking — A method for ranking fragments by how much novel information they give about protein targets in fragment screens. When using the results of fragment screens on many diverse targets, this method has been shown to select a set of functionally diverse fragments that can get information more efficiently from new targets.

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• Filter inference — A scalable nonlinear mixed effects inference approach for snapshot time series data.
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