Inverse Quantum Chemistry: Concepts and Strategies for Rational Compound Design

The rational design of molecules and materials is becoming more and more important. With the advent of powerful computer systems and sophisticated algorithms, quantum chemistry plays an important role in rational design. While traditional quantum chemical approaches predict the properties of a predefined molecular structure, the goal of inverse quantum chemistry is to find a structure featuring one or more desired properties. Herein, we review inverse quantum chemical approaches proposed so far and discuss their advantages as well as their weaknesses.Comment: 43 pages, 5 figure

Gradient-Driven Molecule Construction: An Inverse Approach Applied to the Design of Small-Molecule Fixating Catalysts

Rational design of molecules and materials usually requires extensive screening of molecular structures for the desired property. The inverse approach to deduce a structure for a predefined property would be highly desirable, but is, unfortunately, not well-defined. However, feasible strategies for such an inverse design process may be successfully developed for specific purposes. We discuss options for calculating 'jacket' potentials that fulfill a predefined target requirement - a concept that we recently introduced [T. Weymuth, M. Reiher, MRS Proceediungs, 2013, 1524, DOI:10.1557/opl.2012.1764]. We consider the case of small-molecule activating transition metal catalysts. As a target requirement we choose the vanishing geometry gradients on all atoms of a subsystem consisting of a metal center binding the small molecule to be activated. The jacket potential can be represented within a full quantum model or by a sequence of approximations of which a field of electrostatic point charges is the simplest. In a second step, the jacket potential needs to be replaced by a chemically viable chelate-ligand structure for which the geometry gradients on all of its atoms are also required to vanish. In order to analyze the feasibility of this approach, we dissect a known dinitrogen-fixating catalyst to study possible design strategies that must eventually produce the known catalyst.Comment: 40 pages, 6 tables, 5 figure

Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods

Multiconfigurational Short-Range Density-Functional Theory for Open-Shell Systems

Many chemical systems cannot be described by quantum chemistry methods based on a singlereference wave function. Accurate predictions of energetic and spectroscopic properties require a delicate balance between describing the most important configurations (static correlation) and obtaining dynamical correlation efficiently. The former is most naturally done through a multiconfigurational (MC) wave function, whereas the latter can be done by, e.g., perturbation theory. We have employed a different strategy, namely, a hybrid between multiconfigurational wave functions and density-functional theory (DFT) based on range separation. The method is denoted by MC short-range (sr) DFT and is more efficient than perturbative approaches as it capitalizes on the efficient treatment of the (short-range) dynamical correlation by DFT approximations. In turn, the method also improves DFT with standard approximations through the ability of multiconfigurational wave functions to recover large parts of the static correlation. Until now, our implementation was restricted to closed-shell systems, and to lift this restriction, we present here the generalization of MC-srDFT to open-shell cases. The additional terms required to treat open-shell systems are derived and implemented in the DALTON program. This new method for open-shell systems is illustrated on dioxygen and [Fe(H2O)6]3+.Comment: 37 pages, 3 figures, 4 tables, 1 appendix and 79 references Changes in v2: 1) Appendix B and reference 81 removed 2) Removed dublicated reference and corrected reference 31. 3) Added spin-charge cross terms to GGA (Appendix A). Code changed accordingly and GGA results recalculated. All GGA results are revised -only small modifications observed. Conclusions are unchange