What do you do if you want to master advanced computational techniques for drug discovery as a bioengineer?

Since you will be dealing with large data sets, familiarity with advanced computational methods like deep learning is essential.

A strong foundation is crucial for success in advanced computational drug discovery. This means having a firm grasp of the fundamental concepts underlying these techniques. This includes understanding how molecules interact at the atomic level (molecular mechanics), predicting how a small molecule binds to a protein target (docking), and using computational methods to sift through vast libraries of candidate drugs to identify promising leads (virtual screening). These core concepts provide the framework on which you can layer more advanced machine learning algorithms and data analysis skills.

1) Learn basics of proteins and biology. 2) Learn python which is the most easy and suitable programming language for drug discovery 3) Learn ML/AI techniques 4) Surf through different drug data banks available online and know how to access them in your code. 5) Combine all these techniques to perform virtual screening 6) Learning MD simulations is a plus

Build a strong foundation with open-source tools like OpenMM for molecular mechanics and RDKit for cheminformatics. These will prepare you for more advanced tasks. For AI and ML, consider user-friendly platforms like TensorFlow or PyTorch to design your models. Complement this with MoleculeNet, a treasure trove of datasets specifically designed for drug discovery tasks, to test and refine your models. Don't forget about the data! Leverage public databases like ChEMBL to train your models and explore data science platforms like KNIME for analysis. Finally, keep an eye on cutting-edge tools like AlphaFold for protein structure prediction and explore AI-powered platforms like Insilico Medicine to streamline your workflow.

Always keep in mind that molecular dynamics simulations provide a dynamic view of drug-target interactions, revealing details that static models might miss. Generative AI, a whole new player, can design novel drug candidates based on known activities, accelerating lead optimization. Combining QSAR or pharmacophore models with machine learning leverages the strengths of both. By integrating external biological data, you can create a more comprehensive picture of a drug's potential, leading to better decision-making. Mastering these advanced techniques empowers you to identify promising drug leads faster, improve accuracy, and ultimately, de-risk the entire drug development pipeline.

Imagine you're developing a new cancer drug. Teaming up with a computational scientist can leverage genetic data to identify patients most likely to benefit, making clinical trials more efficient. A biochemist can help you tweak your lead compound's structure to improve its delivery within the body, ensuring it reaches its target. Even a physicist can contribute! Their expertise in molecular simulations can help predict how your drug interacts with cellular membranes, potentially revealing unintended effects early on and saving you valuable time and resources. By combining your drug discovery knowledge with the unique strengths of other scientific disciplines, you can create a more robust development pipeline.

Some things that are missing from our ability to model the behavior of drugs in vivo: - differential PK/PD in tissues - TdS terms that correspond to the path of ligand binding and displacement of solvent - everything else in equations we ignore when we want to make our lives easier Future research is driven by what doesn't work now, and it's hard to know what tools will work best tomorrow. Keep your eye on the goal and the tools will present themselves.