Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states

Abstract

Modern semiempirical electronic structure methods have considerable promise in drug discovery as universal "force fields" that can reliably model biological and drug-like molecules. Herein, we compare the performance of several NDDO-based semiempirical (MNDO/d, AM1, PM6 and ODM2), density-functional tight-binding based (DFTB3, GFN1-xTB and GFN2-xTB) models with pure machine learning potentials (ANI-1x and ANI-2x) and hybrid quantum mechanical/machine learning potentials (AIQM1 and QDπ) for a wide range of data computed at a consistent ωB97X/6-31G* level of theory (as in the ANI-1x database). This data includes conformational energies, intermolecular interactions, tautomers, and protonation states. Additional comparisons are made to a set of natural and synthetic nucleic acids from the artificially expanded genetic information system (AEGIS). This dataset has important implications in the design of new biotechnology and therapeutics. Finally, weexamine acid/base chemistry relevant for RNA cleavage reactions catalyzed by small nucleolytic ribozymes and ribonucleases. Overall, the recently developed QDπ model performs exceptionally well across all datasets, having especially high accuracy for tautomers and protonation states relevant to drug discovery.

Modern Alchemical Free Energy Methods for Drug Discovery Explained

“…Emerging technologies that promise to improve the accuracy and precision of AFE calculations in drug discovery include the development of end-state ensemble reservoirs that can be used within networks to ensure consistent end states as well as continued evolution of sampling methods. Finally, there are a number of exciting advances in the development of new force fields that promise higher levels of accuracy, most notably classical polarizable force fields − and machine learning potentials (MLPs). − Of particular promise are the new methods that combine fast, approximate quantum-mechanical (QM) models with MLP corrections to achieve high accuracy (QM/Δ-MLPs). ,− These methods are “universal” in the sense that unlike molecular mechanical force fields (including polarizable force fields), they do not assume a predetermined bonding topology and are able to accurately model different tautomers and protonation states. This is highly significant, as 30% of the compounds in vendor databases and 21% drug databases have potential tautomers , and it has been estimated that up to 95% of drug molecules contain ionizable groups …”

“…There are a vast number of outstanding issues that are being actively addressed by a variety of groups at the forefront of the field. Some selected for mention here include the following: Methods to handle interfacial and buried (kinetically trapped) water molecules that can fluctuate in occupancy upon binding. − Charge-changing ligand perturbations and counterbalancing salt effects. − Alternative tautomers , and protonation states of both ligand and target molecules. ,, Binding sites involving metal–ligand interactions. , Covalent inhibition. , As the methods evolve to meet these and other challenges, it will become increasingly important to perform large-scale assessments , and conduct community-wide blind challenges. , …”

Exploring Routes to Enhance the Calculation of Free Energy Differences via Non-Equilibrium Work SQM/MM Switching Simulations Using Hybrid Charge Intermediates between MM and SQM Levels of Theory or Non-Linear Switching Schemes

“…As can be seen in Figure 1 a, the quantity of interest and the solvation free energy difference at the high SQM/MM level of theory (dashed, black arrow in Figure 1 a), can also be obtained in three steps according to In addition to computing the solvation free energy at the force field (MM) level of theory (solid black arrow), two correction steps, accounting for the free energy difference of X in the gas phase and the aqueous solution between the two levels of theory (green and red arrows), are required. The development in this field over recent years shows that there is not only enormous potential, but also a high degree of interest in the FES community to use this strategy to compute free energy differences at the SQM/MM levels of theory [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

Training machine learning potentials for reactive systems: A Colab tutorial on basic models

“…In the Behler-Parrinello neural network (BPNN) 17 and its ANI variants, [18][19][20] for instance, symmetric functions are used to encode the local environment of each atom into a descriptor called an atomic environment vector (AEV). In the DeepPot-SE models, [21][22][23][24][25] on the other hand, embedding neural networks are used to transform the coordinates into descriptors. These and other descriptors (such as the internal coordinates, 26 Coulomb matrix, 27 permutation invariant polynomial, 5,14,28,29 bag of bonds, 30 normalized inverted internuclear distances, 31 FCHL representation, 32 and weighted symmetry functions 33 ) are then used as inputs to a regressor, such as a neural network or a kernelbased regressor, to predict the target molecular energy and the corresponding atomic forces.…”