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Virtual screening and modelling: must it be atoms?. Virtual screening and modelling: must it be atoms?. Tim Clark Computer-Chemie-Centrum Universität Erlangen-Nürnberg. What are molecules?. A Paradigm-shift?. Atoms in molecules (AIM).
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Virtual screening and modelling: must it be atoms? Virtual screening and modelling: must it be atoms? Tim Clark Computer-Chemie-Centrum Universität Erlangen-Nürnberg
Atoms in molecules (AIM) • An approximation that relies on the transferability of properties of atoms and groups between molecules • This requires transferability of the electron density assignable to an atom or group • Follows from the first Hohenberg-Kohn theorem • Made popular in the ab initio community by Richard Bader
AIM in virtual screening and modelling • Fingerprints • Fragment models • Similarity (usually) • Topological indices and descriptors • Graph theory • Atomic charge models • Force fields • Scoring functions • Generalised Born solvation models
Scoring functions • Desolvation free energies are probably at least as large as the complexation energy • Two-center scoring increments assume transferable desolvation energies on both sides • Is it any wonder we don’t have a global scoring function? • Why do we accept that scoring functions are local?
AIM in similarity searching • Almost all classical methods are based on the bonding graph • Carbo and Hodgkin indices are an exception • They therefore find very similar bonding graphs • May miss similar molecules • Discriminate against scaffold hops
AIM in modelling: force fields • We usually use all-atom models • United-atom force fields are limited and sacrifice accuracy • All-atom models have two major disadvantages: • They scale badly • They introduce high-frequency vibrational motion that doesn´t interest us • Short time steps • Use SHAKE to remove (!) • Vibrational partition function plays no role in the quantities that interest us
AIM in modelling:electrostatics • Most force fields use point atomic multipoles • Lead to two-center terms inseparable from dispersion/steric repulsion • Overpolarise at short distances • Are not properly shielded at long distances • Must use fictitious and unphysical dielectric constants
Can we abandon AIM? • Means moving to exclusively 3D methods • No comfortable solution to the conformation problem:
Can we abandon AIM? • We need to know where the hydrogens are • Which tautomer(s) are present in solution and bound to the receptor? • Requires • Systematic tautomer searching • Very accurate pKa models
What do we need? • Fast accurate generation of molecular surfaces • Most consistent are isodensity surfaces • These require the electron density (but not necessarily quantum mechanics)
What do we need? • Ways to manipulate surfaces and surface properties quickly and efficiently • Spherical harmonics • Critical points • Visual pattern recognition? • PC-games technology (hardware and software)?
What do we need? • Local properties to describe intermolecular interactions • Molecular electrostatic potential for Coulomb-interactions • Donor-acceptor? • Dispersion?
What do we need? • Intermolecular energy functions • Surface-surface overlap • Electrostatic, donor-acceptor, dispersion, repulsion • If we include polarizability, these can be parameterised using ab initio data
What do we need? • Anisotropic united-atom force field • Monte-Carlo only needs energies • Molecular dynamics needs: • MD in torsional coordinates • Forces for anisotropic united atoms
What do we need? • Surface-integral free energies • Critical for scoring functions, which otherwise use the force-field intermolcular energies • Provide an attractive alternative to descriptor-plus-interpolation QSPR-models • Solvation , lattice energies ?, vapour pressures , partition coefficients ?, solubilities ?.....
Competence • Aberdeen • Spherical-harmonic surfaces, manipulation, superposition, docking • Erlangen • Quantum mechanics, local properties, surface-integral models, modelling • Oxford • Pattern-recognition • Portsmouth • Chemometrics, mapping, conformational searching • Southampton • Classical MD, sampling, pattern-recognition, free energies