One step retrosynthesis of drugs from commercially available chemical building blocks and conceivable coupling reactions

Babak Mahjour, Felix Katzenburg, Emil Lammi, and Tim Cernak (2025)
Highlighted by Jan Jensen

What are important reactions that we currently can't perform? I asked myself this a few years ago and found that there were very few papers in the literature that addressed this. It turns out that I possessed the skills to figure it out for myself if I had only had the idea. The idea being that "the most valuable couplings would utilize the most abundant building blocks to form the most common types of bonds found in [a] target dataset."

As an example, the authors took a list of 9028 known drugs and asked how many could potentially be made in a single step from molecules in the MilliporeSigma catalog by hypothetical coupling reactions. The answer turns out to be 2573 (28%), which is a surprisingly large number. The most common reaction was the coupling of alkyl alcohols and alkyl amines, followed by alkyl acid-alkyl amine and alkyl acid-alkyl alcohols. All reaction for which there's no robust and generally applicable synthetic protocol, although AFAIK, although Zhang and Cernak took a stab at the alkyl acid-alkyl amine coupling. 

I really wish there were more papers like this. Identifying important questions to work on is just as important as solving them, and the latter is almost always a communal effort.


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Open Graph Benchmark: Datasets for Machine Learning on Graphs

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec. arXiv:2005:00687v1
Contributed by Jesper Madsen

A diverse collection of datasets for use in ML applications to graphs has been collected by Hu et al. The Benchmark is intuitively structured and includes evaluation protocols and metrics. Furthermore, the authors have reported the measured performance of a few popular approaches within each application (e.g., ROC-AUC,PRC-AUC, hits, or accuracy). There are several datasets in all three classes of tasks: Node property prediction (ogbn-), link property (ogbl-) prediction, and graph property prediction (ogbg-). 

Of particular interest to those of us who work in biochemistry broadly defined are the SMILES molecular graphs adapted from MoleculeNet [2] such as ogbg-molhiv (HIV) and ogbg-pcba (PubChem Bio Assay); however, also ogbl-ppa (Protein-Protein Association) and ogbn-proteins (Protein-Protein Association) are of interest. Note that MoleculeNet is not included in its entirety - far from it. So, that resource is definitely also interesting to have a close look at if you have not already explored it.

If you are the competitive type, your efforts can be submitted to scoreboards at the hosting website: https://ogb.stanford.edu

[1] Hu, Fey, Zitnik, Dong, Ren, Liu, Catasta, Leskovec. (2020) arXiv:2005:00687v1
[2] MoleculeNet: a benchmark for molecular machine learning. Wu, Ramsundar, Feinberg, Gomes, Geniesse, Pappu, Leswing, Pande. Chem Sci (2018) 9: 513
[3] https://ogb.stanford.edu (2020)

The logic of translating chemical knowledge into machine-processable forms: A modern playground for physical-organic chemistry

Karol Molga, Ewa P. Gajewska, Sara Szymkuć, and Bartosz A. Grzybowski (2019)

Highlighted by Jan Jensen

Figure 11 from the paper (c) RSC

This paper offers a, to me, fascinating "look behind the scenes" of Chematica. At the core this program has 75,000 handcrafted reaction rules (SMARTS and Reaction SMARTS strings as shown in the above figure) extracted from the literature (which took over a decade). The authors estimate that there ca 3000-5000 new reaction classes/types appearing in the literature each years and "that there are on the order of 100,000 distinct reaction classes constituting the body of modern organic chemistry. So their work is almost done :).

The paper does a really excellent job of outlining the challenges involved in constructing these rules and present several cases where the rules must be augmented by ML, MM, and Hückel calculations in order to take non-local structural (e.g. strain and steric hindrance) and electronic effects (e.g. on regioselectivity) into account. Such calculations must be done on the millisecond time scale as many thousand intermediates must be inspected during a retrosynthetic search. At the same time they must be very accurate as inaccuracies accumulate with each step on the retrosynthetic path.

It will be very interesting to see if purely ML-based alternatives can beat this approach!


This work is licensed under a Creative Commons Attribution 4.0 International License.