Can anyone recommend any online courses on the topic? Entry level, introductory, suitable for 1st year students more or less.

Hello eveyone I'm a 4th undergrad student and I'm thinking of applying for a professional diploma is computational drug discovery so I'm asking what are some of the entry level jobs I can apply into after taking this diploma?

We are a group of computer scientists and computational chemists from Stanford. We're developing software tools for ultra-large scale virtual screening and lead optimization. Our platform is significantly cheaper than existing platforms (e.g., Schrodinger, OpenEye, etc.) and has comparable performance and tools (docking, scoring, MD simulations, etc).

We are interested in connecting with people to be first users of the platform. In particular, we'd love to chat with people at biotech companies doing early stage drug discovery work. We are also open to providing white glove services (i.e., running screens, training custom models on internal data, and performing lead optimization manually). Please reach out if you're interested in collaborating!

https://www.biorxiv.org/content/10.1101/2024.08.10.607459v1

My Masters thesis relates to computational drug design and I have to design DOGS (Design of Genuine Structures), CATS (Chemically Advanced Template Search), and SPiDER (Self-organizing map-based Prediction of Drug Equivalence Relationships) algorithms.

I have no background in coding besides basic Python programming and I've only studied 2 courses of biochemistry during my undergrad. Unfortunately, these algorithms are not publicly available (they have been designed by the ETH Zurich team and it's also mentioned on the inSili . com website). I am completely lost on how to go about completing my thesis and generating my drugs.

Furthermore, I am also unsure on how to compare chemical reactions mentioned in the DOGS algorithm paper ("DOGS: Reaction-Driven de novo Design of Bioactive Compounds" by Markus Hartenfeller, Heiko Zettl, Miriam Walter, Matthias Rupp, Felix Reisen, Ewgenij Proschak, Sascha Weggen, Holger Stark, and Gisbert Schneider (2012)) and the ones used to generate my template drugs and decide whether I should add more reactions or not.

Simply put, I only need some mentoring on how to go about doing all of this.

I am a biotech graduate ,used to work in clinical research but quiet my job to start out in drug designing as I wasn't finding time to do it properly ,so can someone please explain me the trajectory to do so ? Any drug design/ discovery experts please guide me.

What Is potential energy surface in the context of computational chemistry and molecule interaction ( molecular docking

I am a junior in high school and I’ve recently been fascinated with molecular modeling, so much so that I want to embark on a competitive inhibitor design project for an oncogenesis-related enzyme. Does anybody here have some advice for how I could go about selecting an enzyme and starting the design?

Any help is great appreciated!

I have 96 ligands and I am wondering how long it takes the program to dock them anyone have an idea?

Dadashkhan et al. used CoreMine Medical as part of their study that identified six genes "as the most significant for MS pathophysiology" and proposed six drugs that target these genes.

Deciphering crucial genes in multiple sclerosis pathogenesis and drug repurposing: A systems biology approach

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"Abstract

This study employed systems biology and high-throughput technologies to analyze complex molecular components of MS pathophysiology, combining data from multiple omics sources to identify potential biomarkers and propose therapeutic targets and repurposed drugs for MS treatment. This study analyzed GEO microarray datasets and MS proteomics data using geWorkbench, CTD, and COREMINE to identify differentially expressed genes associated with MS disease. Protein-protein interaction networks were constructed using Cytoscape and its plugins, and functional enrichment analysis was performed to identify crucial molecules. A drug-gene interaction network was also created using DGIdb to propose medications. This study identified 592 differentially expressed genes (DEGs) associated with MS disease using GEO, proteomics, and text-mining datasets. 37 DEGs were found to be important by topographical network studies, and 6 were identified as the most significant for MS pathophysiology. Additionally, we proposed six drugs that target these key genes. Crucial molecules identified in this study were dysregulated in MS and likely play a key role in the disease mechanism, warranting further research. Additionally, we proposed repurposing certain FDA-approved drugs for MS treatment. Our in silico results were supported by previous experimental research on some of the target genes and drugs. SIGNIFICANCE: As the long-lasting investigations continue to discover new pathological territories in neurodegeneration, here we apply a systems biology approach to determine multiple sclerosis's molecular and pathophysiological origin and identify multiple sclerosis crucial genes that contribute to candidating new biomarkers and proposing new medications.

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Keywords: Drug repurposing; MS pathogenesis; Multiple sclerosis; Protein-protein interaction networks; Systems biology."

Recently, I noticed a trend towards building generative models for a specific target to increase novelty and synthetic accessibility rates, however there are also many existing drugs that are yet to be explored. Can someone explain me the advantages of generative models?

Pharmaceutical science

I want to ask for opinions on the most effective ways to target neuroinflammation using intranasal ROA.

This book chapter gives a good idea of why targeting neuroinflammation could be so promising (the book can be downloaded with nexus telegram bot) - http://doi.org/10.1007/978-981-19-7376-5_20 This paper gives a good idea of why I chose intranasal route of administration (ROA) - https://doi.org/10.1177/1533317513518658

Nose to brain connection allows drugs to skip the Blood-Brain-Barrier (BBB) and go directly to the brain, which normally is not possible with other routes of administration.

So far my "most promising conditate" is Intranasal Meloxicam. Here are my reasons:

1. The daily oral dose is only 15mg, which is the lowest mg dose compared to other NSAIDs. This makes it easy to use Intranasally since intranasal dose will likely be even smaller.

2. Intranasal Meloxicam is being actively researched right now in preclinical trials. So other researchers also think it is a good idea it seems - https://doi.org/10.1016/j.ijpharm.2023.122594

3. Meloxicam 10mg/ml solution is for sale in pharmacies.

So, are there other "good candidate" drugs which could be used to target neuroinflammation by exploiting nose to brain connection that allows to skip BBB? I will gladly answer any questions!

Hello, I hope that all of you are doing well. I recently started working with MOE but I have some doubts regarding the preparation of a ligands database, I already have washed and minimized the energy of the database but I don´t know how to protonate the ligands. I hope that you can help me with this problem. Thanks.

As the title suggests, I want to keep up with a few academic journals beyond the big names like Nature and Science. I have a formal research background in both molecular cell biology and computer science, and my current thesis has a nice amount of wet lab and computational work. I am literate enough in both fields to have no problem following related publications.

With that in mind, I'm wondering what people here may recommend are good journals to actively follow in the drug design and drug discovery sphere. I can access the journals through my institution, typically, so I'm just trying to compile a list that I can actively review monthly.

For reference, I have identified the following journals:

• Nature Reviews Drug Discovery
• Nature Biotechnology
• Drug Discovery Today
• Cell Stem Cell
• Pharmacological Reviews
• Genome Medicine
• EMBO Molecular Medicine
• Journal of Medicinal Chemistry
• Molecular Therapy
• Protein and Cell

Is there anything that you might add/remove? I want to keep the list concise without sacrificing too much on the scope if that makes sense.

I work on natural product discovery as lead candidates using virtual screening methods (ML based QSAR, pharmacophore, molecular docking) for my PhD. However, I'm aware this not exactly drug design and I would like to start learning it as I major in biology with little background on chemistry. I have read about bioisoteric replacements and I'm curious if there are other strategies that I can adopt for a specific protein to optimize the binding affinity/interactions and/or pharmacokinetic properties. Any insight is appreciated.

Is there anyone that is an expert or know how to use maestro? Please message me! I have couple of questions to ask thank you!

Hello, is there anyone that can help me to identify the binding sites of DHFR protein (PDB ID: 2W9G) which interacts with ligand NADP?

Please feel free to message me!

Doing a blind dock and then figuring out which conformation fits i.e ligand binding anywhere to the protein ? I did a blind docking and I got binding energy -10 , can I say that ligand as good inhibitor? And In case of binding site docking ,my pocket size is smaller but ligand is quite big ,will that affect binding affinity?

New free drug design website, based on the content of Molecular Conceptor. More than 40 worldwide experts in their fields have contributed to its contents. https://www.drugdesign.org/

Table of content Drug Design, Molecular Docking, Protein Structure, Bioisoterism, Database Searching, QSAR, 3D-QSAR, Molecular Dynamics, Molecular Geometry, Cheminformatics, Synthesis of Drugs, Library Design, Molecular Similarity, Structure Activity Relationships, Peptidomimetics, Success Stories in Drug Discovery, ADME Properties, Structural Bioinformatics, Encoding Molecules, Introduction to Drug Discovery, Principles in Pharmacophore Elucidation, Introduction to Protein-Ligand Binding, Ligand-Based Approaches, Principles of Structure-Based Design,Molecular Geometry, Molecular Properties, Stereochemistry, Molecular Energies, Molecular Graphics, Conformational Analysis, Case Studies in SAR Analyses, Case Studies in Library Design, Case Studies in ADME/Tox Predictions, Case Studies of Docking in Drug Discovery, Case Studies in Advanced Analog Design, Case Studies in 3D Mimic Design, Case Studies in Peptidomimetics, Case Studies in 3D Database Searching, Examples of Pharmacophores, Case Studies in Structure-Based Design, Case Studies in QSAR and 3D-QSAR, Selected Examples in 3D Analysis