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Benchmarking Small Molecule Forcefields at the Scale of NIST

Kyle A. Beauchamp, Choderalab@MSKCC
Slides here: goo.gl/rKGhzZ

Forcefields: Are we there yet?

Water and protein: getting better

Small molecule forcefields need work

Data access is killing forcefields

Forcefields should be consistent with all available data
Most datasets are heterogeneous, offline, and static

WANTED: Reliable, machine-readable, open archive of physicochemical measurements!

NIST Thermodynamics Research Center

Data Capture at NIST/TRC: ThermoML

ThermoML is rapidly growing

Can we leverage ThermoML for forcefield validation?

Density and dielectric constants as forcefield tests

Sensitive to nonbonded parameters
Simple ensemble average geometric interpretation

$$\rho = \langle \frac{M}{V} \rangle$$

$$\epsilon = 1 + \frac{4\pi}{3} \frac{\langle \mu \cdot \mu \rangle - \langle \mu \rangle \cdot \langle \mu \rangle}{V k_B T}$$

How many measurements are there?

Munging the ThermoML with pyxb and pandas

Benchmarking neat liquid densities and dielectric constants

OpenMM 6.3
GAFF / AM1-BCC (Antechamber + OpenEye)
Converge each density to 0.0002 g / mL ($\approx$ expt. error)

Densities are in the ballpark

Static dielectric constants are consistently underestimated

Fixed charges fail to capture polarizability

Observed: $\epsilon \approx 2.2$, Predicted: $\epsilon \approx 1.001$, $\Delta \Delta G_{solv} \approx$ 2 kcal / mol

Atom counting predicts molecular polarizability to within 2%

$$\alpha = 1.53 n_C + 0.17 n_H + 0.57 n_O + 1.05 n_N + 2.99 n_S + \\ 2.48 n_P + 0.22 n_F + 2.16 n_{Cl} + 0.32 $$

$$\epsilon_{corrected} = \epsilon_{MD} + 4 \pi N \frac{\alpha}{\langle V \rangle}$$

Empirical atomic polarizability corrections reduce bias

Continuous integration of forcefields

Live update stream now available at trc.nist.gov/RSS/

Where do we go from here?

Scale up, real-time simulation, web frontend
Perform new experiments in automated wetlab
Bayesian (MCMC) forcefield / experimental design
Polarizable forcefields

Conclusions

Small molecule forcefields need help
ThermoML is a NIST-supported, machine-readable, and growing set of physicochemical data
We built a semi-automated benchmark of densities and dielectric constants in ThermoML
Empirical polarizability model improves comparisons to measured dielectric constants

Funding and Acknowledgments

Julie Behr (MSKCC)
Patrick Grinaway (MSKCC)
Bas Rustenburg (MSKCC)
John Chodera (MSKCC)
Kenneth Kroenlein (NIST)