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Valuable addition to the modern chemist’s toolkit.

HArD (HeteroAryl Descriptors) - a comprehensive DFT-based database of >31,500 heteroaryl substituents spanning diverse ring systems and regioisomers, each annotated with rich steric, electronic, and aromaticity descriptors.
Their introduction of σₕₑₜ, a Hammett-type constant derived from computed carboxylic acid pKₐ values, nicely extends the classical Hammett framework to heteroaromatic chemistry.
I’ve often leaned on traditional tables and Ertl's tool for aryl substituents - but this work is a real step forward for quantitatively capturing heteroaryl electronic effects.

Interactive Database: https://hard.pengliugroup.com/
Paper: https://www.nature.com/articles/s41597-025-05198-z

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This paper presents a web-based digital tool for virtually screening and optimizing aqueous liquid–liquid extraction, with interactive visualization of speciation, partitioning, and extraction efficiency across pH and solvent space. While the authors built it in Streamlit, the workflow can be easily reproduced in Jupyter notebooks. A must-read if you’re exploring selective extraction strategies.

https://pubs.rsc.org/en/content/articlelanding/2025/dd/d5dd00104h

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This paper from Coley's group presents FlowER, a forward reaction prediction model that treats chemistry as electron redistribution and explicitly respects mass conservation. Unlike black-box product predictors, it generates full mechanistic pathways, making results more interpretable and adaptable even to new reaction classes. An interesting step toward more reliable and mechanistically aware forward synthesis prediction.

https://arxiv.org/abs/2502.12979
https://www.nature.com/articles/s41586-025-09426-9

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Lilly’s digital group has a reputation for going big when it comes to process design prediction, and this paper is a great example focused on crystallisation. The authors show how you can go from a chemical structure all the way to predicting polymorphs, solubility, morphology, and even growth behaviour - basically a digital shortcut to designing a crystallisation process.
If you don’t feel like reading a whole textbook on crystallisation theory, this piece gives you the essentials in a form that’s directly useful for process chemists (especially those with a materials science background)

https://pubs.acs.org/doi/10.1021/acs.cgd.3c01390

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Which reminds me about really practical workflow for figuring out how impurities end up in crystals - whether through surface adsorption, inclusions, or solid solutions - using just a few straightforward experiments.

The authors show with several API case studies that once you know the exact mechanism, you can target purification strategies much more effectively instead of relying on endless trial-and-error. A great read if you’ve ever had to wrestle with impurity headaches during crystallisation.

https://pubs.acs.org/doi/10.1021/acs.oprd.0c00166

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Nice BO use case from ETH Zurich & Novo Nordisk: they co-optimize mAb formulations across Tm, kD, and air–water interface stability, finding good excipients combination in ~33 experiments while keeping pH and osmolality in check.
Still some room for refinement, but great to see BO moving beyond the small-molecule domain.

https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.5c00591

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Large-scale cheminformatics analysis by Ertl et al. (SAscore author) maps how ring systems used in medicinal chemistry evolved over time, highlighting which heterocycles dominate today’s drug space and which are fading.
The study links shifts in ring popularity to synthetic accessibility and changing design strategies, offering a data-driven view of scaffold trends valuable for modern drug discovery.

https://chemrxiv.org/engage/chemrxiv/article-details/6891a60123be8e43d6d10ab0

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The aforementioned SAscore and its successor BR-SAscore - still the best heuristic for synthetic accessibility, imho. Neither the modern SCscore, RAscore, nor SYBA manage to beat it. Very handy if you want to estimate which AI-slop molecules actually have a chance of being synthesised.

https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8

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ICH is merging all stability guides into one big chunk - Q1A–E/Q5C will now live together as just Q1.
What’s new:
– Enhanced emphasis on knowledge- and risk-based approaches
– New or expanded types of stability / supportive studies (f… finally)
– Lifecycle / post-approval changes & commitments
– New Annex for ATMPs and other new modalities

A must-read for the grown-ups in pharma development - you’ll want to be ready for those GAP assessments.🥲

https://www.ema.europa.eu/en/ich-q1-guideline-stability-testing-drug-substances-drug-products

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Cheeky robots are now coming for lab coats too, not just IT jobs.
An interesting preprint introduces RAISE - a self-driving lab that fully automates formulation, contact-angle measurement, and optimization in a Bayesian closed loop. It turns surface science from tedious manual work into rapid, data-driven formulation discovery. Not pharma per se, but it could easily be applied there.

https://arxiv.org/abs/2510.06546

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Not exactly groundbreaking, but a smart and pragmatic shift from Iktos.
Instead of asking “how do we make this designed molecule?” the authors flip the question to “what can we actually make from what’s already on the shelf - and what can we feed to the robots without endless reconfiguration?”
Their cluster synthesis strategy groups diverse reactions into a few shared condition clusters, streamlining execution and cutting setup pain. It’s less about grand design and more about making the most of what’s physically doable - a mindset that feels very relevant for real-world drug discovery labs.

https://chemrxiv.org/engage/chemrxiv/article-details/68de33daf2aff167708137a8 (preprint)

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This review summarizes key challenges and considerations in translating machine learning models into decision-making tools for real-world drug discovery projects, in particular, related to compound toxicity and safety. This includes making choices about data, modeling, validation, model metrics, and applying the model thus obtained to the process of drug discovery.

https://pubs.acs.org/doi/10.1021/acs.chemrestox.5c00033

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The preprint from Bayer applies WISP (Workflow for Interpretability Scoring using matched molecular Pairs) to real chemical datasets like LCAP yields, Factor Xa inhibition, and AMES mutagenicity. It shows how explainability methods can highlight which structural changes (e.g. adding a methyl group) influence model predictions. Importantly, WISP can also reveal when these explanations don’t reflect real chemistry, helping to spot weak or misleading models.

Think of it as a structured way to peek inside the black box and check whether the model is actually learning chemistry or just patterns in the data.

https://chemrxiv.org/engage/chemrxiv/article-details/68bb381ea94eede154ed44f8

Building ChemInformatic Agents with LangGraph
A hands-on introduction to agents and tool calling

- Build and customize AI agents that can reason, plan, and execute tasks in chemical research
- Use tool calling to connect models with cheminformatics libraries
- Explore real-world use cases like property prediction

https://colab.research.google.com/drive/1nuuVA-1RTLqUC2AyKBc1OHmfPzdhhyJG

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Quality by digital design to accelerate sustainable medicines development

If you get tired of QbD, now some people invent QbDD.
Jokes aside - good review of what was already done and how QbD can evolve in digital era.

https://doi.org/10.1016/j.ijpharm.2025.125625

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oligowiki is a curated, queryable database focused on therapeutic oligonucleotides and the chemistries that define them

https://www.oligowizard.com/wiki/

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It reminds me of another crowdsourced database collecting small-molecule synthesis routes.

https://chemistrybydesign.oia.arizona.edu/

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There is always room to read about crystallisation development!
This paper presented the general idea of using rapid process modeling as a parallel instrument to the widely applied DoE-based design and scale-up.
Kind of overkill, but interesting to read for understanding how process could be described.

https://pubs.acs.org/doi/10.1021/acs.oprd.4c00199