@xlpan.bsky.social
📤 10
📥 39
📝 4
qMol: A Web Server for Efficient Molecular Queries Using Fragment-Based Reduced Graphs | Journal of Chemical Information and Modeling
pubs.acs.org/doi/10.1021/...
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qMol: A Web Server for Efficient Molecular Queries Using Fragment-Based Reduced Graphs
Computational tools for searching molecular databases accelerate lead identification in drug discovery. In this work, we introduce qMol, an online platform designed to enable the search for accessible...
https://pubs.acs.org/doi/10.1021/acs.jcim.5c02195
about 1 month ago
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reposted by
Jan H. Jensen
about 2 months ago
I’m hiring 1-year postdoc position in computational chemistry at the University of Copenhagen The research is focusing on automated reaction prediction in collaboration with two major Pharma companies (see e.g.
doi.org/10.1002/anie...
) Please share
#compchem
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SiteMatcher: A Web Server for Structure-Based Drug Design Using Protein–Ligand Interaction Patterns | Journal of Chemical Information and Modeling
pubs.acs.org/doi/full/10....
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SiteMatcher: A Web Server for Structure-Based Drug Design Using Protein–Ligand Interaction Patterns
With the rapid growth of structural data in the Protein Data Bank, efficient mining and utilization of protein–ligand interaction pattern information from these structures can advance rational drug de...
https://pubs.acs.org/doi/full/10.1021/acs.jcim.5c02173
about 2 months ago
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reposted by
Greg Landrum
2 months ago
This week's
#RDKit
blog post looks at LOBSTER, a nice molecular superposition data set that came out last year. Now that everything's loaded into a database using lwreg, I can start playing with the data in future posts.
greglandrum.github.io/rdkit-blog/p...
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Working with the LOBSTER Data set I – RDKit blog
Registering and working with a 3D data set using lwreg
https://greglandrum.github.io/rdkit-blog/posts/2025-11-08-working-with-lobster-1.html
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reposted by
AiChemist MSCA DN
3 months ago
Join the Second Joint Machine Learning Challenge to predict the optical properties of small molecules, transmittance and fluorescence, using screening data for 100k compounds Two winning teams will each receive a €1k prize during SLAS2026. Join
ochem.eu/static/chall...
& submit models by 15 Jan 2026
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EU-OPENSCREEN and SLAS Launch the Second Joint Machine Learning Challenge
EU-OPENSCREEN and the Society for Laboratory Automation and Screening (SLAS) are pleased to announce the second EU-OPENSCREEN/SLAS Joint Machine Learning Challenge, inviting scientists worldwide to pa...
https://www.eu-openscreen.eu/newsroom/eu-openscreen-news/ansicht/eu-openscreen-and-slas-launch-the-second-joint-machine-learning-challenge.html
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reposted by
Adrian Roitberg
3 months ago
If you used our ANI MLIPs, you probably used our TorchANI library. Now, new and improved version 2.0. Use it, enjoy it, break it, let us know what you did or tried to do with it.
doi.org/10.1021/acs.jcim.5c01853
@ignaciopickering.bsky.social
@nickterrel.bsky.social
@khuddleston.bsky.social
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TorchANI 2.0: An Extensible, High-Performance Library for the Design, Training, and Use of NN-IPs
In this work, we introduce TorchANI 2.0, a significantly improved version of the free and open source TorchANI software package for training and evaluation of ANI (ANAKIN-ME) deep learning models. Tor...
https://pubs.acs.org/doi/10.1021/acs.jcim.5c01853
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reposted by
6 months ago
Special Issue "AI in Drug Discovery" highlights how advanced machine learning enhances structural-based drug discovery, molecular property forecasting, and chemical reaction prediction. Enjoy reading the editorial
rdcu.be/ezXFl
as well as access all articles at
www.biomedcentral.com/collections/...
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Advanced machine learning for innovative drug discovery - Journal of Cheminformatics
This editorial presents an analysis of the articles published in the Journal of Cheminformatics Special Issue “AI in Drug Discovery”. We review how novel machine learning developments are enhancing st...
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01061-w
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reposted by
AiChemist MSCA DN
6 months ago
Igor Tetko will give lecture "OCHEM - platform for winning Challenges!" at OpenTox Summer School 22/07 at 13:00 CET
opentox.net/events/opent...
. Join and participate to this event and/or use materials at
aichemist.eu/summerschool
to try your skills to develop models used at a previous challenge.
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reposted by
Gina El Nesr
10 months ago
Protein function often depends on protein dynamics. To design proteins that function like natural ones, how do we predict their dynamics?
@hkws.bsky.social
and I are thrilled to share the first big, experimental datasets on protein dynamics and our new model: Dyna-1! 🧵
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reposted by
Frank Noe
6 months ago
@maxhenrybarnhart.bsky.social
has written a really nice piece on BioEmu for Chemical & Engineering news.
cen.acs.org/biological-c...
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Microsoft AI predicts protein conformations
The open-source tool goes beyond AlphaFold by finding proteins’ multiple equilibrium states and free energies
https://cen.acs.org/biological-chemistry/proteomics/Microsoft-AI-predicts-protein-conformations/103/web/2025/07
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reposted by
AiChemist MSCA DN
7 months ago
It was a pleasure to overview winning strategies to build machine learning models during the Erasmus Mundus Summer School on Chemoinformatics
molekule.net/css2025/
at Ljubljana. Many thanks organisers for a great scientific and cultural program and interesting interactions with students and speakers
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reposted by
Jimmy 🇩🇰 🇨🇭
7 months ago
Roche is hiring a small-molecule computer-aided drug design researcher in Basel, Switzerland. Go for it!
#rdkit
#compchem
#chemsky
www.linkedin.com/jobs/view/42...
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Roche hiring Scientist in Small Molecule Computer-Aided Drug Design (CADD) in Basel, Basel, Switzerland | LinkedIn
Posted 12:44:22 PM. At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture…See this and similar jobs on LinkedIn.
https://www.linkedin.com/jobs/view/4260858154
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reposted by
Olexandr Isayev 🇺🇦 🇺🇸
7 months ago
My group is on🔥,2nd
@chemrxiv.bsky.social
preprint in a week! Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials.
#compchem
collaboration with Marom lab
@cmu.edu
chemrxiv.org/engage/chemr...
#compchem
#chemsky
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reposted by
CompBioPhys
7 months ago
😍 Lisa's sketch illustrates her passion for
#GPCRs
: Don't miss her most recent
@chemicalscience.rsc.org
📜 publication about "Identification of allosteric sites and ligand-induced modulation in the dopamine receptor through large-scale alchemical mutation scan"🔗
doi.org/10.1039/D4SC...
#compchem
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ThermoSeek: An Integrated Web Resource for Sequence and Structural Analysis of Proteins from Thermophilic Species | Journal of Chemical Information and Modeling
pubs.acs.org/doi/10.1021/...
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ThermoSeek: An Integrated Web Resource for Sequence and Structural Analysis of Proteins from Thermophilic Species
Protein engineering is a critical area within biotechnology, with enhancing protein thermal stability posing a significant challenge. Proteins from organisms adapted to extreme temperatures, such as t...
https://pubs.acs.org/doi/10.1021/acs.jcim.5c00010
7 months ago
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reposted by
Nathalie M. Grob
7 months ago
We are hiring! Check out our open PhD position for an exciting industry collaboration with Novo Nordisk:
jobs.ethz.ch/job/view/JOP...
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PhD Position in Peptide-Based Drug Discovery (Industry Collaboration, w/m/d)
https://jobs.ethz.ch/job/view/JOPG_ethz_VDsg3FQ2US7auYPZjh
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reposted by
Greg Landrum
8 months ago
This week I have updated and revised an old blog post showing how to perform extended Hueckel calculations with the
#RDKit
. This is a fun one for me because it involves work I did back in grad school. :-)
greglandrum.github.io/rdkit-blog/p...
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Doing extended Hueckel calculations with the RDKit – RDKit blog
Including an exploration of charge variability across conformers
https://greglandrum.github.io/rdkit-blog/posts/2025-05-30-eHT-in-RDKit.html
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reposted by
AiChemist MSCA DN
8 months ago
The second article describing group winning model of
#Tox24
challenge co-organised with
@aidd.bsky.social
was just published by
@pubs.acs.org
pubs.acs.org/doi/10.1021/...
Congratulations to Xiaolin Pan
@xlpan.bsky.social
and his co-authors! Do not miss reading about strategies how to win Challenges!
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Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge
Transthyretin (TTR) plays a vital role in thyroid hormone transport and homeostasis in both the blood and target tissues. Interactions between exogenous compounds and TTR can disrupt the function of t...
https://pubs.acs.org/doi/10.1021/acs.chemrestox.4c00560
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reposted by
Greg Landrum
9 months ago
This week's
#RDKit
blog post revisits and updates a really old post looking at the most common chemical "words".
greglandrum.github.io/rdkit-blog/p...
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Common chemical words – RDKit blog
Borrowing an idea from Randall Munroe
https://greglandrum.github.io/rdkit-blog/posts/2025-05-09-common-chemical-words-1.html
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reposted by
Olexandr Isayev 🇺🇦 🇺🇸
9 months ago
Long & windy road of academic publishing! Few journal rejections and two years (!!!) after preprint, AIMNet2 paper was just published
@chemsocrev.rsc.org
With 69 citations to it as of now, it's immediately part of 2025 HOT🌶️ Article collection.
pubs.rsc.org/en/content/a...
#chemsky
#compchem
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reposted by
Oxford Protein Informatics Group (OPIG)
9 months ago
MolSnapper has been published in
@pubs.acs.org
Journal of Chemical Information and Modeling! MolSnapper integrates expert knowledge into diffusion models for structure-based drug design using conditioning Congratulations Yael Ziv, Fergus Imrie, Brian Marsden, and Charlotte Deane
shorturl.at/8PeWT
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MolSnapper: Conditioning Diffusion for Structure-Based Drug Design
Generative models have emerged as potentially powerful methods for molecular design, yet challenges persist in generating molecules that effectively bind to the intended target. The ability to control the design process and incorporate prior knowledge would be highly beneficial for better tailoring molecules to fit specific binding sites. In this paper, we introduce MolSnapper, a novel tool that is able to condition diffusion models for structure-based drug design by seamlessly integrating expert knowledge in the form of 3D pharmacophores. We demonstrate through comprehensive testing on both the CrossDocked and Binding MOAD data sets that our method generates molecules better tailored to fit a given binding site, achieving high structural and chemical similarity to the original molecules. Additionally, MolSnapper yields approximately twice as many valid molecules as alternative methods.
https://pubs.acs.org/doi/10.1021/acs.jcim.4c02008
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reposted by
pen(Taka)
9 months ago
pubs.acs.org/doi/10.1021/...
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Tertiary Alcohol: Reaping the Benefits but Minimizing the Drawbacks of Hydroxy Groups in Drug Discovery
Among the smaller substituents in the medicinal chemist’s toolbox, the hydroxy (OH) group can bestow one of the largest impacts in the drug-like properties of a molecule. A previous study showed that an H-to-OH structural modification effectively decreases lipophilicity, increases solubility, and decreases hERG inhibition. Despite these benefits, an OH group is not always recommended in drug molecules because it presents a metabolic “soft spot” for oxidation and glucuronidation in primary and secondary alcohols. Furthermore, the OH group presents challenges in permeability. In contrast, tertiary alcohols (3° ROH) often display an improved metabolic profile because oxidation at the 3° ROH is not possible, and the geminal alkyl groups could sterically shield the OH group from glucuronidation and permeability challenges. Through a series of matched molecular pairs, this Perspective highlights the 3° ROH as a motif that can reap the benefits but minimize the drawbacks of hydroxy groups in drug discovery.
https://pubs.acs.org/doi/10.1021/acs.jmedchem.4c03078?ref=PDF
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reposted by
Jan H. Jensen
9 months ago
New preprint: Finding Drug Candidate Hits With a Hundred Samples: Ultra-low Data Screening With Active Learning
doi.org/10.26434/che...
#compchem
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reposted by
Oxford Protein Informatics Group (OPIG)
10 months ago
We're recruiting a 3-year postdoc for the Novo Nordisk - Oxford Fellowship programme! Develop machine learning approaches for fragment library design and experimental optimisation With
@fergusimrie.bsky.social
and Charlotte Deane Job advert:
shorturl.at/3l47e
Further details:
shorturl.at/u4UkK
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Job Details
https://shorturl.at/3l47e
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reposted by
pen(Taka)
10 months ago
Nonadditive SAR analysis
#cheminformatics
#rdkit
#mmpa
I visited San Diego last week and have opportunity to discuss with lots of researchers. It was really a great experience for me. And I could get positive feedback from my blog post. BTW, Sometime medicinal chemist try to conbine positive…
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Nonadditive SAR analysis #cheminformatics #rdkit #mmpa
I visited San Diego last week and have opportunity to discuss with lots of researchers. It was really a great experience for me. And I could get positive feedback from my blog post. BTW, Sometime medicinal chemist try to conbine positive transformation for compound optimization. For example adding Cl atom to phenyl ring improve potency of comound A (it's comound B) and replace carbon atom to nitrogen improve potency of compound (it's compound C), next we would like to add Cl atom and replace carbon atom to nitrogen to generate compound D.
https://iwatobipen.wordpress.com/?p=5787
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reposted by
Jan H. Jensen
10 months ago
Revealing the Relationship between Publication Bias and Chemical Reactivity with Contrastive Learning
pubs.acs.org/doi/10.1021/...
#compchem
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Revealing the Relationship between Publication Bias and Chemical Reactivity with Contrastive Learning
A synthetic method’s substrate tolerance and generality are often showcased in a “substrate scope” table. However, substrate selection exhibits a frequently discussed publication bias: unsuccessful ex...
https://pubs.acs.org/doi/10.1021/jacs.5c01120
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reposted by
Jan H. Jensen
10 months ago
#compchem
add a skeleton here at some point
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reposted by
AiChemist MSCA DN
11 months ago
What a fantastic line-up! But wait, there's more! 💫 Check out the full speaker list at
www.cecam.org/workshop-det...
. Registration is open until the 28th of March 📅 Don't miss out!
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reposted by
Pat Walters
12 months ago
Machine Learning in Drug Discovery Resources page updated for 2025.
github.com/PatWalters/r...
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GitHub - PatWalters/resources_2025: Machine Learning in Drug Discovery Resources 2024
Machine Learning in Drug Discovery Resources 2024. Contribute to PatWalters/resources_2025 development by creating an account on GitHub.
https://github.com/PatWalters/resources_2025
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reposted by
Jan H. Jensen
12 months ago
#compchem
add a skeleton here at some point
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reposted by
Jan H. Jensen
about 1 year ago
SMARTpy: a Python package for the generation of cavity steric molecular descriptors and applications to diverse systems
doi.org/10.1039/D4DD...
#compchem
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SMARTpy: a Python package for the generation of cavity steric molecular descriptors and applications to diverse systems
Steric molecular descriptors designed for machine learning (ML) applications are critical for connecting structure–function relationships to mechanistic insight. However, many of these descriptors are...
https://doi.org/10.1039/D4DD00329B
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reposted by
Polaris
about 1 year ago
🏁 The antiviral challenge is live! 🏁 Ready to test your skills on new data? Hosted in partnership with
@asapdiscovery.bsky.social
and
@omsf.io
, we've prepared detailed notebooks showcasing how to format your data and submit your solutions. 🧑💻
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reposted by
Jan H. Jensen
about 1 year ago
ACES-GNN: Can Graph Neural Network Learn to Explain Activity Cliffs? | ChemRxiv -
doi.org/10.26434/che...
#compchem
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ACES-GNN: Can Graph Neural Network Learn to Explain Activity Cliffs?
Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug dis...
https://doi.org/10.26434/chemrxiv-2025-11wfv?utm_campaign=shareaholic&utm_medium=bluesky&utm_source=socialnetwork
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Excited to share latest work from Changge Ji's group, MacGen, a cutting-edge web tool for structure-based macrocycle design. We believe MacGen will accelerate the exploration of macrocycle space and open new avenues in drug discovery. Try it out for free at
macgen.xundrug.cn
!
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XPharm@tinyboat
Web site created using create-react-app
https://macgen.xundrug.cn
about 1 year ago
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1
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reposted by
Michael Bronstein
about 1 year ago
After two years, our paper on generative models for structure-based drug design is finally out in
@natcomputsci.bsky.social
www.nature.com/articles/s43...
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Structure-based drug design with equivariant diffusion models - Nature Computational Science
This work applies diffusion models to conditional molecule generation and shows how they can be used to tackle various structure-based drug design problems
https://www.nature.com/articles/s43588-024-00737-x
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reposted by
Jan H. Jensen
about 1 year ago
PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method | ChemRxiv -
doi.org/10.26434/che...
#compchem
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PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method
Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physi...
https://doi.org/10.26434/chemrxiv-2024-3nwwv-v3?utm_campaign=shareaholic&utm_medium=bluesky&utm_source=socialnetwork
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reposted by
Philippe Schwaller
about 1 year ago
We are hiring (resharing appreciated)! Given recent successful grant applications (I got my SNSF Starting Grant 🚀), we are extending the LIAC team with multiple openings (PhD/postdoc) for 2025. Apply now (deadline: December 20th) by filling in this form:
forms.fillout.com/t/eq5ADAw3kkus
.
#ChemSky
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