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Hey! If you're at the fall meeting of the ACS on August 20, check out Anna's presentation on generating protein-protein interactions using QuADD.

Structure-based virtual screening (vs) is a powerful technique to rapidly identify hit molecules across a broad chemical space against a target of interest. Using BRD4-BD1 as a model system to showcase our vs workflow, our computational chemistry team identified the ideal crystal structure for docking, validated the docking protocol and proceeded to screen using Enamine Ltd. drug like library to identify 20 hits that were screened using our inhouse SPR platform. This identified 2 single-digit uM hits. Read our poster here https://lnkd.in/ej-brtRS #computationalchemistry #cadd #screening #drugdiscovery

A timely J Med Chem editorial (no pun intended) and I can remember the days when you could only do this on a computer: Ma et al (2024) Rational Molecular Editing: A New Paradigm in #DrugDiscovery #SynChem #MedChem #DrugDesign #OpenAccess https://lnkd.in/eAtHK_Wk

The fall meeting of the American Chemical Society is just around the corner, and we are really excited to be giving three presentations at the event. On Tuesday, our Computational Chemist Dr. Anna Petroff will be presenting her work on generating protein-protein interactions using our Quantum-Aided Drug Design (QuADD) platform. #QuADD is able to utilize the advanced #optimization potential of today's #quantum computers to search a massive molecular space of over 10^30 options - identifying lead-like compounds in a matter of minutes rather than months. If you are interested in finding out more, connect with POLARISqb today. Schedule a meeting with our team here: https://lnkd.in/gXXH7kwK

AI in drug discovery and pharmacutical development, Protein Production and Downstream Processing, R&D. Looking for new opportunities

🧪 Free Online Molecular Docking Hands-on Workshop with AutoDock 4 Hosted by: RBT 🗓️ Date: October 6, 2024 ⏰ Time: 7:00 PM - 9:00 PM IST Register today! 📌 https://lnkd.in/dG3ncGDV 🔬 Agenda: 1. Introduction to Molecular Docking: Overview of the Drug Discovery Pipeline. Introduction to Molecular Docking and its Key Concepts. 2. Preparing the Target Receptor: Downloading the Receptor Structure from the PDB Database. Cleaning and Preparing the Receptor (e.g., removing water molecules, adding hydrogens, adding charge). 3. Ligand Preparation: Ligand Preparation using AutoDockTools. Adding Polar Hydrogens, Charges, and Converting to pdbqt Format. 4. Defining the Active Site for Docking (Grid Parameter File Generation): Defining the Receptor and Ligand for Grid Parameter File Generation. Extracting Coordinates to define the Grid Box Center. Generating the Final Grid Parameter File. 5. Running the Docking: Selecting the Docking Algorithm and generating the Docking Parameter File. Executing the Final Docking Run. 6. Docking Results Overview: Reviewing Docking Scores (Binding Affinities) for the Ligand. Extracting and Analyzing the Best Binding Modes and Docking Scores from output files. 7. Visualizing Binding Modes: Using Maestro or Online Tools to Visualize Ligand-Receptor Interactions. Identifying Key Interactions (e.g., Hydrogen Bonds, Hydrophobic Interactions, Pi-Pi Stacking). 8. Docking Validation: Superimposing Docked Poses with the Native Ligand to check RMSD Values. Ensuring Accurate Docking Results through Validation. 🌟 Join us for this interactive session and enhance your skills in molecular docking! 📌 https://lnkd.in/dG3ncGDV Register today to secure your spot and dive into the fascinating world of drug discovery! #MolecularDocking #DrugDiscovery #AutoDock #Bioinformatics #FreeWorkshop #HandsOnTraining #LifeSciences

on the October 6 will be an online free 2 hours workshop on Molecular Docking. #drugdiscovery #AIfordrugdiscovery #computationalbiology #bioinformatics

Structure-based virtual screening (vs) is a powerful technique to rapidly identify hit molecules across a broad chemical space against a target of interest. Using BRD4-BD1 as a model system to showcase our vs workflow, our computational chemistry team identified the ideal crystal structure for docking, validated the docking protocol and proceeded to screen using Enamine Ltd. drug like library to identify 20 hits that were screened using our inhouse SPR platform. This identified 2 single-digit uM hits. Read our poster here https://lnkd.in/ej-brtRS #computationalchemistry #cadd #screening #drugdiscovery

Predicting the price of molecules using their predicted synthetic pathways Opportunity to have a matrics that would allow chemists to accelerate the decision-making process related to the cost-of-goods is as important as having QSAR models, docking scores, diverse druggability metrics, and synthetic feasibility scores that currently allows to refine and filter libraries of virtual molecules in order to prioritize their synthesis Not any more as now there exists a function which estimates the price of a novel virtual molecule and which takes into account the availability and price of starting materials has never been considered before https://lnkd.in/gEEM4isF

Why does this Matter? This matters because it addresses a significant gap in the field of computational chemistry. By incorporating the cost of synthesizing molecules into the decision-making process, this approach can significantly improve the efficiency and effectiveness of drug discovery and materials science. The development of RetroPriceNet demonstrates the potential of deep learning models in predicting the price of molecules based on their synthetic pathways, ultimately enhancing decision-making related to cost-of-goods and accelerating the drug discovery process.

I’m happy to share that I’ve obtained a new certification: Molecular Modelling Workshop from MolSoft LLC! In the workshop, l got the opportunity to try out the following modules along with an in-depth discussion on the technologies behind Molsoft, such as the ICM and the BPMC, which make the tool fast and accurate. 1.Protein Preparation 2.ICM Pocket Finder 3.ICM-dock for docking small molecule 4.Detailed analysis of docking results 5.ICM 3D ligand editor 6.Virtual screening and hit list generation 6.ADMET property prediction 7.QSAR 8.Pharmacaphore generation.

Predicting the price of molecules using their predicted synthetic pathways Opportunity to have a matrics that would allow chemists to accelerate the decision-making process related to the cost-of-goods is as important as having QSAR models, docking scores, diverse druggability metrics, and synthetic feasibility scores that currently allows to refine and filter libraries of virtual molecules in order to prioritize their synthesis Not any more as now there exists a function which estimates the price of a novel virtual molecule and which takes into account the availability and price of starting materials has never been considered before https://lnkd.in/gEEM4isF

Optimize deconvolution processing methods in Agilent OpenLab CDS for robust molecular weight confirmation of synthetic oligonucleotides. https://bit.ly/4ce7qsh #oligonucleotide#oligodeconvolution#oligo

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Optimize deconvolution processing methods in Agilent OpenLab CDS for robust molecular weight confirmation of synthetic oligonucleotides. Watch video: https://bit.ly/3PoCNa6

I’m happy to share that I’ve obtained a new certification: Molecular Modelling Workshop from MolSoft LLC! In the workshop, l got the opportunity to try out the following modules along with an in-depth discussion on the technologies behind Molsoft, such as the ICM and the BPMC, which make the tool fast and accurate. 1.Protein Preparation 2.ICM Pocket Finder 3.ICM-dock for docking small molecule 4.Detailed analysis of docking results 5.ICM 3D ligand editor 6.Virtual screening and hit list generation 7.ADMET property prediction 8.QSAR 9.Pharmacaphore generation.