Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.     

Molecular motions in drug design: the coming age of the metadynamics method

Metadynamics is emerging as a useful free energy method in physics, chemistry and biology. Recently, it has been applied also to investigate ligand binding to biomolecules of pharmacological interest. Here, after introducing the basic idea of the method, we review applications to challenging targets for pharmaceutical intervention. We show that this methodology, especially when combined with a variety of other computational approaches such as molecular docking and/or molecular dynamics simulation, may be useful to predict structure and energetics of ligand/target complexes even when the targets lack a deep binding cavity, such as DNA and proteins undergoing fibrillation in neurodegenerative diseases. Furthermore, the method allows investigating the routes of molecular recognition and the associated binding energy profiles, providing a molecular interpretation to experimental data.     

Representations and descriptors unifying the study of molecular and bulk systems

Establishing a unified framework for describing the structures of molecular and periodic systems is a long-standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need for such a method. This perspective aims to discuss the development and use of three promising approaches—topological, atom-density, and symmetry-based—for the prediction and rationalization of physical, chemical, and mechanical properties of atomistic systems across different scales and compositions.     

Mass spectrometric approaches for elucidation of antigenantibody recognition structures in molecular immunology

Mass spectrometric approaches have recently gained increasing access to molecular immunology and several methods have been developed that enable detailed chemical structure identification of antigen-antibody interactions. Selective proteolytic digestion and MS-peptide mapping (epitope excision) has been successfully employed for epitope identification of protein antigens. In addition, "affinity proteomics" using partial epitope excision has been developed as an approach with unprecedented selectivity for direct protein identification from biological material. The potential of these methods is illustrated by the elucidation of a beta-amyloid plaque-specific epitope recognized by therapeutic antibodies from transgenic mouse models of Alzheimer's disease. Using an immobilized antigen and antibody-proteolytic digestion and analysis by high resolution Fourier transform ion cyclotron resonance mass spectrometry has lead to a new approach for the identification of antibody paratope structures (paratope-excision; "parex-prot"). In this method, high resolution MS-peptide data at the low ppm level are required for direct identification of paratopes using protein databases. Mass spectrometric epitope mapping and determination of "molecular antibody-recognition signatures" offer high potential, especially for the development of new molecular diagnostics and the evaluation of new vaccine lead structures.     

Fuzzy definition of molecular fragments in chemical structures

This paper presents a methodology for seeking the relationships between chemical substructures (molecular fragments) and spectral parameters using a computer collection data of molecular spectra. To establish the spectrum-structure correlations, the program has to search the chemical structure base in order to find compounds containing a given molecular fragment in the molecule. There exists no sole definition of a substructure, as it always depends on the type of problem dealt with. In the problem of structural identification, fuzzy definitions of substructures are applied, and their forms are imposed by the spectral methods used.     

Biosynthesis of new polymers of controlled molecular structure

This paper describes the use of artificial genes to direct bacterial synthesis of new polypeptides of precisely defined molecular structure. This approach has been applied to three problems in polymer chemistry and physics: i) control of chain folding in crystalline polymers, ii) biosynthesis of polypeptides containing unnatural amino acids, and iii) preparation of monodisperse helical polymers.     

A novel subshape molecular descriptor

Molecules with similar shapes and features often have similar biological activity. Several computational approaches search chemical databases for new leads or templates based on overall molecular shape similarity. However, active molecules often present critical subshapes that are required for binding, which may be missed by comparing overall shape similarity. We present a new approach to compare molecular shapes of different sizes and to calculate subshape similarity. We developed a skeletal representation of the shape which is topologically unrelated to covalent chemical connectivity. This simplifies rotational and translational sampling. We test initial possible alignments by matching similar triangles. This triangle-matching filter rapidly eliminates most geometrically impossible matches. Surviving matches are filtered further in successive stages. These stages involve direction, feature, and shape matching procedures. Our approach is applied to several situations demonstrating lead discovery and evolution.     

Machine learning of solvent effects on molecular spectra and reactions

Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics/molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design.

A machine learning approach for modeling the influence of external environments and fields on molecules has been developed, which allows the prediction of various types of molecular spectra in vacuum and under implicit and explicit solvation.     

Graph-based molecular alignment (GMA)

We describe a combined 2D/3D approach for the superposition of flexible chemical structures, which is based on recent progress in the efficient identification of common subgraphs and a gradient-based torsion space optimization algorithm. The simplicity of the approach is reflected in its generality and computational efficiency: the suggested approach neither requires precalculated statistics on the conformations of the molecules nor does it make simplifying assumptions on the topology of the molecules being compared. Furthermore, graph-based molecular alignment produces alignments that are consistent with the chemistry of the molecules as well as their general structure, as it depends on both the local connectivities between atoms and the overall topology of the molecules. We validate this approach on benchmark sets taken from the literature and show that it leads to good results compared to computationally and algorithmically more involved methods. The results suggest that, for most practical purposes, graph-based molecular alignment is a viable alternative to molecular field alignment with respect to structural superposition and leads to structures of comparable quality in a fraction of the time.     

Thinking Like a Chemist: Intuition in Thermoelectric Materials

The coupled transport properties required to create an efficient thermoelectric material necessitates a thorough understanding of the relationship between the chemistry and physics in a solid. We approach thermoelectric material design using the chemical intuition provided by molecular orbital diagrams, tight binding theory, and a classic understanding of bond strength. Concepts such as electronegativity, band width, orbital overlap, bond energy, and bond length are used to explain trends in electronic properties such as the magnitude and temperature dependence of band gap, carrier effective mass, and band degeneracy and convergence. The lattice thermal conductivity is discussed in relation to the crystal structure and bond strength, with emphasis on the importance of bond length. We provide an overview of how symmetry and bonding strength affect electron and phonon transport in solids, and how altering these properties may be used in strategies to improve thermoelectric performance.     

Is there a quantum definition of a molecule?

The paper surveys how chemistry has developed over the past two centuries starting from Lavoisier’s classification of the chemical elements at the end of the eighteenth century; the subsequent development of the atomic–molecular model of matter preoccupied chemists throughout the nineteenth century, while the results of the application of quantum theory to the molecular model has been the story of this century. Whereas physical chemistry originated in the nineteenth century with the measurement of the physical properties of groups of chemical compounds that chemists identified as families, the goal of chemical physics is the explanation of the facts of chemistry in terms of the principles and theories of physics. Chemical physics as such was only possible after the discovery of the quantum theory in the 1920’s. By then the first of the sub‐atomic particles had been discovered and seemingly it is no longer possible to discuss chemical facts purely in terms of atoms and molecules – one has to recognize the electron and the nucleus, the parts of atoms. The combination of classical molecular structure with the quantum properties of the electron has given us a tremendously successful account of chemistry called ‘quantum chemistry’. Yet from the perspective of the quantum theory the deepest part of chemistry, the existence of chemical isomers and the very idea of molecular structure that rationalizes it, remains a central problem for chemical physics. This revised version was published online in July 2006 with corrections to the Cover Date.     

Scalable fine-grained parallelization of plane-wave-based ab initio molecular dynamics for large supercomputers

Many systems of great importance in material science, chemistry, solid-state physics, and biophysics require forces generated from an electronic structure calculation, as opposed to an empirically derived force law to describe their properties adequately. The use of such forces as input to Newton's equations of motion forms the basis of the ab initio molecular dynamics method, which is able to treat the dynamics of chemical bond-breaking and -forming events. However, a very large number of electronic structure calculations must be performed to compute an ab initio molecular dynamics trajectory, making the efficiency as well as the accuracy of the electronic structure representation critical issues. One efficient and accurate electronic structure method is the generalized gradient approximation to the Kohn-Sham density functional theory implemented using a plane-wave basis set and atomic pseudopotentials. The marriage of the gradient-corrected density functional approach with molecular dynamics, as pioneered by Car and Parrinello (R. Car and M. Parrinello, Phys Rev Lett 1985, 55, 2471), has been demonstrated to be capable of elucidating the atomic scale structure and dynamics underlying many complex systems at finite temperature. However, despite the relative efficiency of this approach, it has not been possible to obtain parallel scaling of the technique beyond several hundred processors on moderately sized systems using standard approaches. Consequently, the time scales that can be accessed and the degree of phase space sampling are severely limited. To take advantage of next generation computer platforms with thousands of processors such as IBM's BlueGene, a novel scalable parallelization strategy for Car-Parrinello molecular dynamics is developed using the concept of processor virtualization as embodied by the Charm++ parallel programming system. Charm++ allows the diverse elements of a Car-Parrinello molecular dynamics calculation to be interleaved with low latency such that unprecedented scaling is achieved. As a benchmark, a system of 32 water molecules, a common system size employed in the study of the aqueous solvation and chemistry of small molecules, is shown to scale on more than 1500 processors, which is impossible to achieve using standard approaches. This degree of parallel scaling is expected to open new opportunities for scientific inquiry.