This guide outlines how to perform Principal Component Analysis (PCA) and compute Free Energy Landscapes (FEL) from molecular dynamics (MD) simulations using GROMACS. These analyses are useful to capture dominant motions and identify energetically favorable states in biomolecular systems.
The future of drug discovery is open, smart, and community-driven. Gone are the days of relying solely on expensive, proprietary platforms, today’s open-source AI tools are transforming how we explore chemical space, and they’re accessible to all.
Molecular Dynamics (MD) simulations allow computational chemists to explore the dynamic behaviour of protein–ligand complexes beyond static structures.
AlphaFold has revolutionized protein structure prediction by achieving unprecedented accuracy in many cases (1). However, even high-quality predicted models should be rigorously validated before use in research.
The blood-brain barrier (BBB) is a vital selective barrier in the central nervous system. Assessing the permeability of compounds across the BBB is crucial for drug development targeting the brain. While clinical experiments are accurate, they are time-consuming and costly. Computational methods offer an alternative for predicting BBB permeability.
Microbes, life’s unseen workhorses, hold immense potential for bioremediation, medicine, and understanding our planet. Yet, their intricate workings remain largely a mystery. This is where microbial genomics steps in, offering a powerful tool to decode their genetic language.
In today’s digital era, the vast expanse of data permeates every aspect of our lives. From online interactions to industrial processes, we find ourselves immersed in an ever-growing sea of information. Yet, the true value of this data lies in the insights it can provide. This is where data science steps in – the art and science of extracting meaningful patterns and insights from data to drive innovation and solve complex problems. Nowhere is this more evident than in the realm of chemistry, where data science is revolutionizing how we understand and manipulate molecules.
PaDELPy is a Python library that integrates the PaDEL-Descriptor molecular descriptor calculation software, allowing efficient generation of molecular fingerprints for machine learning-based quantitative structure-activity relationship (QSAR) models in drug discovery.
Machine learning models, created by training algorithms to recognize data patterns, can be either supervised or unsupervised, applied in classification, regression, and more. Here, we’ll explore using PaDELPy to generate fingerprints that are crucial in predictive QSAR modeling, specifically targeting molecular activity prediction within the HCV Drug dataset.
Hey there! Today, let’s talk about Molecular Dynamics (MD) simulation and how it can help us understand the behavior of complex molecular systems. MD simulation works by creating an initial configuration of a system, including the positions, velocities, and masses of all atoms or molecules. The simulation then calculates the forces acting on each particle based on their interactions with other particles in the system.
This tutorial will guide you step-by-step through the process of setting up and running a molecular dynamics simulation. It’s assumed that you have a basic understanding of working with the command line, including basic commands and file navigation. Ideally, you should also have some experience using bash on a Linux system, since many of the instructions and commands in this tutorial are specific to this environment. Whether you’re a beginner or an experienced user, this tutorial will provide you with valuable insights and practical knowledge to help you successfully conduct your molecular dynamics simulation.
The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis. New approaches have proved to show improvement and accelerate the joint drug discovery and development processes.
The ever-increasing bioactivity data that are produced nowadays allow exhaustive data mining and knowledge discovery approaches that change chemical biology research. A wealth of cheminformatics tools, web services, and applications therefore exists that supports a careful evaluation and analysis of experimental data to draw conclusions that can influence the further development of chemical probes and potential lead structures.
The ChEMBL database is a manually curated resource of bioactive molecules with drug-like properties, integrating chemical, bioactivity, and genomic data to facilitate the translation of genomic information into new drugs.