Drug Marvel

Drug discovery and development rely heavily on identifying suitable drug targets within the human proteome. It's crucial to find "druggable" targets, i.e., those that can bind drugs effectively. Once identified, the challenge becomes locating druggable pockets within these targets. Predicting pocket druggability can open a new avenue of possible novel drug intervening mechanisms through interacting with novel binding sites within targets of interest which can be considered the future of drug design! In this blog post we will shed light on the process and some useful tools used in pocket druggability assessment!
Read the full article at 👉: https://drugmarvel.wordpress.com/2024/03/11/finding-druggable-pockets-in-druggable-targets/

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Molecular docking and molecular dynamics simulations are invaluable tools in drug discovery and understanding protein-ligand interactions. Central to these techniques are accurate force fields, mathematical models that describe the interactions between atoms in a molecule.

But how to choose the right force field for your simulations? This is an age-old question in the world of molecular simulations, which imposes a real challenge for even the most seasoned researcher. This crucial choice can make or break the accuracy and reliability of your results.

But don't let the force field frenzy scare you. In this blog post we will guide you and help you to choose wisely and simulate confidently with the most suitable force field for your research question.
https://drugmarvel.wordpress.com/2023/12/20/the-force-field-frenzy-choosing-the-right-one-for-your-molecular-simulations/

And remember, the molecules are always waiting to tell their stories! 😊

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Happy simulating!

When working with protein structures from the Protein Data Bank (PDB), the same protein might have multiple chains, differing in quality indicators. To determine which chain to use for analysis, two sections in the “Full Report” of the PDB structure should be examined: the “Overall Quality” section for a general idea, and the “Torsion Angles” section for a detailed view. Typically, the chain with higher overall quality, more analyzed residues, and fewer "Ramachandran" and "sidechain" outliers is the better choice. This method provides a straightforward way to choose the optimal chain for research studies like docking or other structure-based studies.

Read the full article at 👉https://drugmarvel.wordpress.com/2023/11/03/which-chain-of-a-multiple-identical-chain-pdb-structure-should-you-use-for-your-docking-study/

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The Protein Data Bank (PDB) is a publicly accessible database with over 210,000 3D structures of biological macromolecules, making it fundamental for structural biology research. However, choosing the most appropriate structure for your target protein can be a challenge. To aid in this process, it's important to consider the following criteria: resolution, which indicates the precision of atom positions in the structure; the structure's completeness; its functional and conformational state; any mutations present; the wwPDB Validation section's quality indications; and factors unique to the experimental technique used. By carefully assessing these factors, one can select a PDB structure tailored to specific research needs.

You can read our comprehensive guide on picking the best PDB structure for your research here 👉https://drugmarvel.wordpress.com/2023/10/20/how-to-pick-the-best-pdb-structure-for-your-target-protein-a-comprehensive-guide/

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Molecular docking techniques use computational algorithms to simulate the binding process between a ligand and a macromolecule target, providing insights into potential drug-target interactions allowing for optimization.
There are several types of molecular docking techniques each has a preferable application setting. For example, blind docking investigates the entire binding site of a protein; cross docking tests different ligand structures against a single protein model; re-docking is used to validate the accuracy of docking protocols. Moreover, undocking simulations provide insights into the stability of drug-target interactions. While ensemble docking accounts for protein flexibility by using multiple protein conformations for docking. In addition to covalent docking, which predicts the binding mode of covalent ligands that interact with their target irreversibly.
You may be wondering which technique should be used, in this article we are going to cover some of the most used techniques to help guide you. However, it is important to keep in mind that the choice of a suitable technique depends on the specific application and available resources.
Read the full article here 👉
https://drugmarvel.wordpress.com/2023/10/13/molecular-docking-techniques-an-overview-of-the-most-commonly-used-jargon/

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Pharmaceutical research hinges on understanding drug targets—molecules involved in disease processes—and their interactions with drugs. Drug targets can be proteins, enzymes, nucleic acids, or other molecules, and drugs bind to these targets to alter their functions for therapeutic effects. Two types of drug-target interactions exist: reversible (common in chronic diseases treatment) and irreversible (common in cancer treatment). Various chemical bond types—ionic interaction, hydrogen bonding, covalent bonding, Van der Waals forces, dipole-dipole interactions, and hydrophobic interactions—are involved in these interactions. Scientists study these complex mechanisms to advance the understanding and development of effective medications. In today's article we will have a quick overview of the most common Drug-target interactions 👉 https://drugmarvel.wordpress.com/2023/10/05/an-overview-on-drug-targets-and-drug-target-interactions/ .

We will dive deeper into this topic in our upcoming course, stay tuned and subscribe to our newsletter to be among the first people to know about its release!

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Designing of new drug candidates requires the balancing of several criteria, such as potency, selectivity, drug-likeness, and ADMET properties. Unfortunately, optimizing for one of these properties may be detrimental to others. Such a problem of potentially conflicting goals can be addressed through multi-objective optimization (MOO) algorithms. In today’s blog post we will shed light on some applications of AI to MOO in Computer-Aided Drug Design (CADD).

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Applications of AI to Multi-objective optimization in Computer-Aided Drug Design Designing of new drug candidates requires the balancing of several criteria, such as potency, selectivity, drug-likeness, and ADMET properties. Unfortunately, optimizing for one of these properties…

In many instances during your CADD project, you maybe wondering if your ligand will suffer from off-target interactions, or will you find common features among your targets in a multi-target ligand setting.
We will address these questions in this Curiosity Lab tutorial👇 by addressing the protein specificity search concept and we will be exploring the BCSpecificitySearch tool to help you in this regard.

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Protein Specificity Search In many instances during designing drug candidates for a certain protein, the whole project maybe hindered as a result of off-target interactions due to the similarity of the binding sites in the t…

In today's blog post, we explore the remarkable applications of AI in computer-aided drug design and discovery (CADD). Discover how this cutting-edge technology is revolutionizing the pharmaceutical industry and changing the game for researchers worldwide.

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How AI is transforming the field of computer-aided drug design and discovery In today’s blog post, we explore the remarkable applications of AI in computer-aided drug design and discovery (CADD). Discover how this cutting-edge technology is revolutionizing the pharmac…