Quantum computing in pharma: A multilayer embedding approach for near future applications.

Quantum computers are special purpose machines that are expected to be particularly useful in simulating strongly correlated chemical systems. The quantum computer excels at treating a moderate number of orbitals within an active space in a fully quantum mechanical manner. We present a quantum phase estimation calculation on F2 in a (2,2) active space on Rigetti's Aspen-11 QPU. While this is a promising start, it also underlines the need for carefully selecting the orbital spaces treated by the quantum computer. In this work, a scheme for selecting such an active space automatically is described and simulated results obtained using both the quantum phase estimation (QPE) and variational quantum eigensolver (VQE) algorithms are presented and combined with a subtractive method to enable accurate description of the environment. The active occupied space is selected from orbitals localized on the chemically relevant fragment of the molecule, while the corresponding virtual space is chosen based on the magnitude of interactions with the occupied space calculated from perturbation theory. This protocol is then applied to two chemical systems of pharmaceutical relevance: the enzyme [Fe] hydrogenase and the photosenzitizer temoporfin. While the sizes of the active spaces currently amenable to a quantum computational treatment are not enough to demonstrate quantum advantage, the procedure outlined here is applicable to any active space size, including those that are outside the reach of classical computation.

Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications.

Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.

Fragment-Based Discovery of a Novel, Brain Penetrant, Orally Active HDAC2 Inhibitor.

Fragment-based ligand discovery was successfully applied to histone deacetylase HDAC2. In addition to the anticipated hydroxamic acid- and benzamide-based fragment screening hits, a low affinity (∼1 mM) α-amino-amide zinc binding fragment was identified, as well as fragments binding to other regions of the catalytic site. This alternative zinc-binding fragment was further optimized, guided by the structural information from protein-ligand complex X-ray structures, into a sub-µM, brain penetrant, HDAC2 inhibitor (17) capable of modulating histone acetylation levels in vivo.

Origin of the Non-Arrhenius Behavior of the Rates of Enzymatic Reactions.

The origin of the non-Arrhenius behavior of the rate constant for hydride transfer enzymatic reactions has been a puzzling problem since its initial observation. This effect has been used originally to support the idea that enzymes work by dynamical effects and more recently to suggest an entropy funnel model. Our analysis, however, has advanced the idea that the reason for the non-Arrhenius trend reflects the temperature dependence of the rearrangements of the protein polar groups in response to the change in the charge distribution of the reacting system during the transition from the ground state (GS) to the transition state (TS). Here we examine the validity of our early proposal by simulating the catalytic reaction of alcohol dehydrogenase (ADH) and determine the microscopic origin of the entropic and enthalpic contributions to the activation barrier. The corresponding analysis establishes the origin of the non-Arrhenius behaviors and quantifies our original suggestion that the classical effect is due to the entropic contributions of the environment. We also find that the quantum effects reflect in part the temperature dependence of the donor-acceptor distance.

The entropic contributions in vitamin B12 enzymes still reflect the electrostatic paradigm.

The catalytic power of enzymes containing coenzyme B12 has been, in some respects, the "last bastion" for the strain hypothesis. Our previous study of this system established by a careful sampling that the major part of the catalytic effect is due to the electrostatic interaction between the ribose of the ado group and the protein and that the strain contribution is very small. This finding has not been sufficiently appreciated due to misunderstandings of the power of the empirical valence bond (EVB) calculations and the need of sufficient sampling. Furthermore, some interesting new experiments point toward entropic effects as the source of the catalytic power, casting doubt on the validity of the electrostatic idea, at least, in the case of B12 enzymes. Here, we focus on the observation of the entropic effects and on analyzing their origin. We clarify that our EVB approach evaluates free energies rather than enthalpies and demonstrate by using the restraint release (RR) approach that the observed entropic contribution to the activation barrier is of electrostatic origin. Our study illustrates the power of the RR approach by evaluating the entropic contributions to catalysis and provides further support to our paradigm for the origin of the catalytic power of B12 enzymes. Overall, our study provides major support to our electrostatic preorganization idea and also highlights the basic requirements from ab initio quantum mechanics/molecular mechanics calculations of activation free energies of enzymatic reactions.

Computer aided enzyme design and catalytic concepts.

Gaining a deeper understanding of enzyme catalysis is of great practical and fundamental importance. Over the years it has become clear that despite advances made in experimental mutational studies, a quantitative understanding of enzyme catalysis will not be possible without the use of computer modeling approaches. While we believe that electrostatic preorganization is by far the most important catalytic factor, convincing the wider scientific community of this may require the demonstration of effective rational enzyme design. Here we make the point that the main current advances in enzyme design are basically advances in directed evolution and that computer aided enzyme design must involve approaches that can reproduce catalysis in well-defined test cases. Such an approach is provided by the empirical valence bond method.

Validating computer simulations of enantioselective catalysis; reproducing the large steric and entropic contributions in Candida Antarctica lipase B.

The prospect for computer-aided refinement of stereoselective enzymes is further validated by simulating the ester hydrolysis by the wild-type and mutants of CalB, focusing on the challenge of dealing with strong steric effects and entropic contributions. This was done using the empirical valence bond (EVB) method in a quantitative screening of the enantioselectivity, considering both k(cat) and k(cat)/K(M) of the R and S stereoisomers. Although the simulations require very extensive sampling for convergence they give encouraging results and major validation, indicating that our approach offers a powerful tool for computer-aided design of enantioselective enzymes. This is particularly true in cases with large changes in steric effects where alternative approaches may have difficulties in capturing the interplay between steric clashes with the reacting substrate and protein flexibility.