PeerJ Physical Chemistry – Highlights Collection

To celebrate the announcement of Professor Jan Jensen (University of Copenhagen) as the new Editor-in-Chief of PeerJ Physical Chemistry, we’ve put together a collection of some of the most cited and viewed articles from the first two-years of the journal. The articles range from chemical space exploration (written by Prof. Jensen and colleagues!) to evaluating compounds that could best inhibit the COVID-19 virus, and demonstrate the breadth of research across all physical chemistry topics covered by the journal. You can see the full Highlights in PeerJ Physical Chemistry collection here, and we’ve included some of the featured articles below.

About PeerJ Physical Chemistry

PeerJ Physical Chemistry is an Open Access, peer-reviewed journal, publishing physical chemistry research articles and literature reviews. PeerJ Physical Chemistry evaluates submissions based only on an objective determination of scientific and methodological soundness, placing an emphasis on research integrity; high ethical standards; constructive peer-review; exemplary production quality; and leading-edge online functionality.

Free publishing in PeerJ Physical Chemistry this August!

We will be waiving the Article Processing Charge for accepted articles that are submitted to PeerJ Physical Chemistry in August this year. We wanted to give you notice of this promotion, but if you’d like to submit sooner please contact the PeerJ Communities Team. Full terms at end.

Chemical space exploration: how genetic algorithms find the needle in the haystack

Emilie S. Henault, Maria H. Rasmussen, Jan H. Jensen​​​​

DOI: 10.7717/peerj-pchem.11/fig-2

We explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom- or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.

The SIR dynamic model of infectious disease transmission and its analogy with chemical kinetics

Cory M. Simon​​​​​

Mathematical models of the dynamics of infectious disease transmission are used to forecast epidemics and assess mitigation strategies. In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics while providing an exposition on the classic Susceptible–Infectious–Removed (SIR) epidemic model. Particularly, the SIR model resembles a dynamic model of a batch reactor carrying out an autocatalytic reaction with catalyst deactivation. This analogy between disease transmission and chemical reaction enables the exchange of ideas between epidemic and chemical kinetic modeling communities.

Benchmarking quantum mechanical methods for calculating reaction energies of reactions catalyzed by enzymes

Jitnapa Sirirak​, Narin Lawan, Marc W. Van der Kamp, Jeremy N. Harvey, Adrian J. Mulholland​​​​​

To assess the accuracy of different quantum mechanical methods for biochemical modeling, the reaction energies of 20 small model reactions (chosen to represent chemical steps catalyzed by commonly studied enzymes) were calculated. The methods tested included several popular Density Functional Theory (DFT) functionals, second-order Møller Plesset perturbation theory (MP2) and its spin-component scaled variant (SCS-MP2), and coupled cluster singles and doubles and perturbative triples (CCSD(T)). Different basis sets were tested. CCSD(T)/aug-cc-pVTZ results for all 20 reactions were used to benchmark the other methods. It was found that MP2 and SCS-MP2 reaction energy calculation results are similar in quality to CCSD(T) (mean absolute error (MAE) of 1.2 and 1.3 kcal mol−1, respectively). MP2 calculations gave a large error in one case, and are more subject to basis set effects, so in general SCS-MP2 calculations are a good choice when CCSD(T) calculations are not feasible. Results with different DFT functionals were of reasonably good quality (MAEs of 2.5–5.1 kcal mol−1), whereas popular semi-empirical methods (AM1, PM3, SCC-DFTB) gave much larger errors (MAEs of 11.6–14.6 kcal mol−1). These results should be useful in guiding methodological choices and assessing the accuracy of QM/MM calculations on enzyme-catalyzed reactions.

Read the Highlights in PeerJ Physical Chemistry collection

Free Publishing – Terms & Conditions

The waiver is only for the APC and applies only to new submissions to PeerJ Physical Chemistry made in August 2022; other charges may still apply (e.g. for articles longer than 40 typeset pages); author services (copy-editing, video and graphical abstracts) are not included. Submissions that are started but not fully submitted, and manuscripts submitted outside of this period, are not eligible. Submissions must be in scope for PeerJ Physical Chemistry. The APC waiver cannot be transferred to any other journal.