AIML-Enabled Drug Discovery of MYC-MAX Inhibitors
AIML-Enabled Drug Discovery of MYC-MAX Inhibitors
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Job Description
High Level Description
MYC a key member of the Mycprotooncogene family plays a crucial role in regulating many cell functions. However it is also the main driver in more than 50% of human cancers making the MYC oncogene one of the most important and soughtafter drug targets in cancer research. Despite years of research there are no clinically viable MYC inhibitor today and MYC has traditionally been described as undruggable due to its intrinsically disordered protein structure which lacks both binding pocket and enzymatic activity. However MYCs function is dependent on heterodimerization with its obligate partner MAX to activate transcription. As a result inhibiting the interaction between MYC and MAX has become a key strategy making the identification of small molecule inhibitors that disrupt this interaction critical in the development of MYCtargeted cancer therapies.
Therefore this thesis shall focus on leveraging AI and Machine Learning algorithms to speedup drug discovery and development by identifying small molecules capable of inhibiting the interaction between the MYC and MAX.
Project Description
In this project a datadriven approach will be used to research and implement the most suitable AI/ML techniques for identifying potential MYCMAX inhibitors.
The thesis will begin with the collection or exploration of existing relevant datasets (if they are not available at the start of the project) focusing on small molecules known to interact with MYCMAX or similar proteinprotein interactions. If necessary the collected data will need to be cleaned and preprocessed to prepare the data for training the machine learning models. Following this feature engineering will be applied to transform the raw chemical structure data into formats that can be understood by machine learning algorithms. The selection of molecular representationssuch as Molecular Descriptors Molecular Fingerprints or graphbased representations like Graph Neural Networks (GNNs)should be carefully evaluated based on the nature of the dataset and the specific problem at hand.
Once these molecular representations are established one or more machine learning models such as classification regression or deep learning models will be developed using appropriate tools and frameworks. The performance of the model will then be evaluated using appropriate metrics and hyperparameter optimization techniques will be employed to finetune the models for optimal performance.
Who are we looking for
We are looking for a masters student with knowledge in Artificial Intelligence Machine Learning and bioinformatics. You should have a passion and a strong interest in applying AI to drug discovery. This thesis is suitable for students pursuing M.Sc. in Medical Biotechnology Molecular Biotechnology Bioinformatics Computational Biology or an equivalent field.
Purpose
The purpose of the thesis is to apply AI and Machine Learning algorithms to identify small molecule inhibitors that disrupt the MYCMAX interaction. The thesis aims to demonstrate how AI/ML can be leveraged to speedup drug discovery and contribute to the fight against cancers driven by MYC.
The thesis project can be published and used in your personal portfolio as well as in company marketing. Include Resum/CV and cover letterin your application.
Employment Type
Full Time
