Making molecules is hard. How can we simplify the predicted synthesis routes of generated, property‑optimized small molecules? Our last work presents a framework that lets you "mix-and-match" multiple reaction constraints in the synthesis of your generated molecules.

Coauthors: @JeffGuo__ , @ZJoncev, @JLuterbacher, @pschwllr . Thanks to @NCCR_Catalysis and @EPFL_ReO GlobaLeaders for the support!.

Using a generalist molecular generative model and reinforcement learning, we can address synthesizability multi-parameter optimization objectives without additional inductive biases. Code: Preprint:

We show the applicability of the framework in different use cases, related to drug discovery, industrial byproduct valorization and ultra-large-scale virtual screening.

✅ Enforce the presence of specific reactions in the synthesis or only certain reactions.❌ Avoid specific reactions.🟢 Enforce the presence of certain building blocks in the synthesis.⬇️ Minimize synthesis route length.

Finally out! We have been working on a generative molecular design framework to allow steerable and granular control over synthetic routes for property-optimized molecules. ⚗️ 🖥️ Take a look below! 👇.

RT @RebeccaNeeser: Hello from Singapore 🇸🇬! Thrilled to be at #ICLR2025 presenting our work on fragment-based drug discovery 🧩. We go beyon….

RT @JeffGuo__: Check out the updated published version of our pre-print in @ChemicalScience!. (1) *General-purpose* generative model + retr….

RT @6ojaHa: 🎉Our BoLudo paper is in @J_A_C_S! Bayesian Optimization for nanocrystaL strUcture Design Optimization (jk, it's BOjana & LUDO😂)….

RT @SchwallerGroup: 🧵 Excellent showing from @SchwallerGroup at #NeurIPS2024! .Our team received multiple spotlight presentations and accep….

RT @AtinaryTech: Atinary @ #NeurIPS in Vancouver this week🍁 .Connect with our #AI #ML researchers @VictorSabanza & @shreyaspadhy. Our rese….

RT @d_armstr: 1/ Starting material constrained synthesis planning is now possible using a general retrosynthesis algorithm *without* trai….

RT @CoryMSimon: 📜 two very interesting, fundamental papers that shed light on when multi-fidelity (MF) Bayesian optimization (BayesOpt) is….

5/ This work is the result of my internship at @AtinaryTech , a collaboration between academia and industry working in an amazing team 🧑‍🎓 🤝🧑‍🔧. With the support of @NCCR_Catalysis and @EPFL_ReO GlobaLeaders program! 💪.

4/ If you want to see the results in detail, check the preprint:.📄: Our paper has also been accepted in the NeurIPS2024 AIDrugX workshop. 🧬💻.

3/ ⚗️ We applied our MFBO guidelines to real-world chemistry and materials benchmarks, where a cheap and informative approximation of the problem is available. The result? MFBO consistently reduced costs and improved outcomes compared to standard BO methods.

2/ 🔍 Our study systematically explores the performance of MFBO across different scenarios, revealing that its success depends on the balance between cost and informativeness of low-fidelity data. When low-fidelity data is both inexpensive and informative, MFBO offers advantages.

1/ 🧪💡 Curious about faster material & molecular discoveries? Multi-fidelity Bayesian Optimization (MFBO) is your friend! In this paper, we investigate when MFBO is truly effective compared to standard BO methods, helping to balance cost & accuracy in optimization campaigns.

RT @NCCR_Catalysis: Just two weeks before we start Phase II of @NCCR_Catalysis! 🎉😍 Earlier this year, we gathered our members & supporters….

RT @JeffGuo__: (Near) mode collapse can be a feature, not a bug! Intentional overfitting can be strategic to improve sample efficiency for….