A practical portfolio of machine learning and cheminformatics workflows for virtual screening and molecular modeling
This repository presents a structured pathway for learning and applying machine learning and deep learning techniques in drug discovery. It combines theoretical resources, hands-on implementations, and small research-style projects focused on virtual screening, molecular representation, and predictive modeling of small molecules.
In addition to practical projects, the repository introduces the fundamental toolkit for machine learning in Python, covering libraries essential for data processing, model development, and cheminformatics. It also includes recommended courses, books, and selected research materials that support understanding and mastering machine learning approaches for drug design, together with tutorials and workflows illustrating applications such as virtual screening and docking.
The majority of the code presented in this repo can be implemented and executed using the open-source MLflow tool. The following outlines some key software packages included in my roadmap for utilizing ML models in drug design.
- Python 3.11.5
- Pandas 2.1.1
- Scikit-Learn 1.2.2
- TensorFlow 2.14.0
- Keras 2.12.0
In this section we delve into essential tools for handling big data with ML models such as :
In this section we explore practical projects and hands-on applications of machine learning and deep learning in drug design, including predictive modeling, molecular data handling, and virtual screening.
This section collects key research papers, tutorials, and educational resources related to machine learning in drug discovery and molecular modeling. The goal is to build a clear understanding of the theoretical foundations behind the methods implemented in this repository, including virtual screening, molecular representations, deep learning architectures, and model evaluation strategies.