Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks

Yu Harabuchi, Ruben Staub, Min Gao, Nobuya Tsuji, Benjamin List, Alexandre  Varnek, and Satoshi Maeda (2026)
Highlighted by Jan Jensen

The automated predict of chemical reaction networks have thus far been limited to relatively small systems, typically with less than 50 atoms (including Hs) due to computational expense. This study goes significantly beyond this by studying a system with 228 atoms.

This is made possible by three things: 

1. While the system is big, the reaction is relatively simple, so the reaction network is relatively small. 

The reaction is an acid-catalysed cyclisation reaction involving a relatively small and chemically simple molecules. It is the (chiral) acid catalyst that contributes most of the atoms. The reaction itself has three steps: protonation of alkene group, intramolecular C-O bond formation on the activated alkene, deprotonation of the O to regenerate the catalyst. Most of the atoms are chemically inert, and there are 12 chemically active atoms (defined by the user). In all, the study identified 74 possible intermediates/products and only about half of those are chemically distinct if you ignore chirality. 

2. Cheap surrogate energy function

They use a Δ-ML approach that corrects the xTB energy and gradient to obtain better accuracy. The ML model is trained on-the-fly against DFT calculations. 

3. Massive computational resources 

In spite of 1 and 2 they this study required massive computational resources. They don't address this point specifically, other than to mention that it requires millions of gradient evaluations, but Maeda stressed this point during his talk at the WATOC last year. 

So this is not exactly a routine application. 

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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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From Random Determinants to the Ground State

Hao Zhang and Matthew Otten (2025)
Highlighted by Jan Jensen


The paper introduces a method they call TrimCI that very efficiently finds a relatively small set of determinants that accurately describes strongly correlated systems. (Well, it actually works for any system, but the main advantage is for strongly correlated systems). 

Unlike most new correlation methods, this one is actually simple enough to describe in a few sentences. TrimCI starts by constructing a set of orthogonal (non-optimised!) MOs (e.g. by diagonalising the AO overlap matrix). From these MOs you construct a small number of random determinants (e.g.100), construct the wavefunction (i.e. construct the Hamiltonian matrix and diagonalise, as per usual). Then you compute all the Hamiltonian elements between this wavefunction ($H_{ij}$) and the remaining determinants and add determinants with sufficiently large |$H_{ij}$| to the wavefunction. Finally, there is the trimming step "which removes negligible basis states by first diagonalising randomised blocks of the core and then performing a global diagonalising step on the surviving set." And repeat.

The authors find that this approach converges much quicker than other similar methods, using many fewer determinants. Another big advantage is that the method does not require a single-determinant ground state as a starting point and is thus not sensitive to how much such a single-determinant deviates from the actual wavefunction.

So, what's the catch here? In order to be practically useful, we need to compute energy differences with mHa accuracy, and I did not see any TrimCI results for chemical systems where the energy had converged to that kind of accuracy. It's possible that error cancellation can help here, but that needs to be investigated. The authors do look at extrapolation, which looks promising, but needs to be systematically investigated. Yet another option is to use the (compact) TrimCI wavefunction as an ansatz for dynamic-correlation methods.

It's also not clear what AO basis set it used for some of these calculations (including the one shown above). I suspect small basis sets are used and even FCI energies with very small basis sets are of limited practical use. Are the TrimCI calculations on large systems still practical with more realistic basis sets?

Nevertheless, this seems like a very promising step in the right direction.


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Electron flow matching for generative reaction mechanism prediction

Joonyoung F. Joung, Mun Hong Fong, Nicholas Casetti, Jordan P. Liles,  Ne S. Dassanayake & Connor W. Coley (2025)
Highlighted by Jan Jensen

While the title says reaction mechanism prediction, it's really reaction mechanism-based reaction outcome prediction. The approach uses glow matching (a generalization of diffusion-based  approaches) to predict changes to the bond-electron (BE) matrix (basically connectivity matrix with the lone pair electron count on the diagonal), thus ensuring mass and charge conservation because changes in the BE matrix are constrained to sum to 0. The method is trained 1.4 million elementary reaction steps derived primarily from the USPTO dataset.  

Recursive predictions yield a complete reaction mechanism step by step, starting from the reactants. (I assume the products are defined as the state where no more changes are predicted.) The method is probabilistic so several different reaction outcomes are possible if the process is repeated, and ranked according to frequency. Another option is to use DFT calculation to rank the different mechanisms.

 

Like any ML method its applicability is tied to the training set. For example, of 22,000 reactions from patents reported in 2024 that were not assigned  a specific reaction class in the Pistachio dataset, the approach successfully recovered products in only 351 cases. However, the authors show that a new reaction class can be added with as few as 32 examples.

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Fundamental Study of Density Functional Theory Applied to Triplet State Reactivity: Introduction of the TRIP50 Dataset

William B. Hughes, Mihai V. Popescu, Robert S. Paton (2025)
Highlighted by Jan Jensen

While this paper presents an interesting and useful benchmark and dataset of barrier heights involving organic molecules in the triplet state, I am highlighting this paper for a different reason. 

While compiling the data set the authors "observed a  common tendency for triplet SCF calculations to converge non-Aufbau solutions, resulting in catastrophic predictions in both thermochemistry and activation energy  barriers and leading to errors as high as 26.4 kcal/mol." They go on to note that "Since such  errors  cannot be predicted a priori, manual inspection of spin densities for triplet-state calculations can be helpful to ensure the lowest triplet state has been converged with KS-DFT."

I remember the days when the SCF would routinely fail to converge even for simple singlet ground state molecules. So when they did converge and you got odd results, one of the first things you checked was the orbitals. But those days are long gone and I don't think it would occur to me now. I'd be much more likely to ascribe it to some deficiency in the functional. 

I now wonder how many of such wrong conclusions are scattered throughout the literature, especially for molecules with "funky" electronic structure, such as transition metal complexes.  Manual inspection of the MOs is not going to be a practical option for many of these studies, and SCF stability checks did not identify all problems! 

However, most QM packages have several options for the MO guess and it might be a good idea to use more than one of them and check whether they all converge to the same SCF solution. It'll be just like the old days.

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UMA: A Family of Universal Models for Atoms

Brandon M. Wood, Misko Dzamba1, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R. Kitchin, Daniel S. Levine, Kyle Michel, Anuroop Sriram, Taco Cohen, Abhishek Das, Ammar Rizvi, Sushree Jagriti Sahoo, Zachary W. Ulissi, C. Lawrence Zitnick (2025)
Highlighted by Jan Jensen

I use xTB extensively in my research and I am often asked why don't switch to machine learning potentials (MLPs) instead. My answer has always been that they have too many limitations: limited atom types, no charged molecules, can't handle reactions, efficiency on CPUs, solvent effects, etc. I know these can be overcome by making bespoke MLPs, then it is not really a simple replacement for xTB, but a whole new workflow. 

However, the new UMA MLP from Meta seems to address all but one of my concerns (more on that below). UMA is trained to reproduce DFT energies and gradients calculated for nearly half a billion 3D structures, spanning molecules, surfaces, reactions, etc, containing atoms from virtually all of the periodic table. It is also possible to specify the charge and multiplicity and the cost seems to be comparable to xTB when running on CPUs, when interfaced with ORCA. So this is all very encouraging.

Two main questions remain. One is the accuracy, and by that I mean how faithfully it reproduces ωB97M-V/def2-TZVPD results (in the case of molecules) for molecules outside it's training set. AFAIK nothing is published yet, but encouraging results are being shared online.

The other main question is how to include implicit solvent effects. In cases where it is OK to optimise i the gas phase, one option is to compute the solvation energy with some other method and add it to the gas phase UMA results. Even if you do that at the DFT level, UMA has still saved you a lot if time. However, if the problem requires optimisation in solvent, then you have to use a faster method like xTB to compute the solvent effects on the gradient in order to get any time-savings. Depending on how well xTB does on the system of interest, this could "contaminate" the UMA results. Alternatively, a purely ML approach would basically amount to redoing UMA for molecules with continuum solvation included. Explicit solvation is fine in principle, but impractical for routine applications.

Anyway, before this is resolved there could be some fairly routine applications that still cannot be address satisfactorily with MLPs.

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Computing solvation free energies of small molecules with first principles accuracy

 J. Harry Moore, Daniel  J. Cole, and Gábor Csányi (2025)
Highlighted by Jan Jensen

Local CCSD(T) has made it possible to reach near chemical accuracy for many real life applications. However, most real life applications happen in solution,  where the only realistic option is still continuum solvation methods, which, in general, do offer chemical accuracy (especially for charged systems). 

In principle, this can be fixed explicit solvation at the CCSD(T) level but of course the need for sampling makes this practically impossible at present. However, this paper by Moore and co-workers is a step in that direction. 

The idea is that ML potentials now approach chemical accuracy and are fast enough for proper sampling. The authors developed a MLP that is compatible with alchemical transformation (needed for sampling efficiency) and showed that experimental logP values (the difference in solvation energy between water and octanol) of drug-like compounds can be predicted within 0.65 kcal/mol (0.45 log units), i.e chemical accuracy.

Furthermore, the calculations took "only" about 4-7 days per molecule (octanol simulations converge slower than water) on a single node, containing either 8 NVIDIA A100, or 8 NVIDIA L40S, GPUs. While this is too slow for routine applications it is fast enough to create benchmark sets. This is great news since experimental solvation energies only are measured for relatively small molecules.

However, there are some caveats. The main one is that only neutral molecules where tested, since the MLP only was trained on neutral compounds, and it is not clear whether the same accuracy can be obtained for charged systems. 

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