Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds.

Volume of distribution at steady state (V) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V directly from structure were evaluated using a large set of clinical compounds.

Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery.

Conventional drug discovery is long and costly, and suffers from high attrition rates, often leaving patients with limited or expensive treatment options. Recognizing the overwhelming need to accelerate this process and increase success, the ATOM consortium was formed by government, industry, and academic partners in October 2017. ATOM applies a team science and open-source approach to foster a paradigm shift in drug discovery.

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery.

One of the key requirements for incorporating machine learning (ML) into the drug discovery process is complete traceability and reproducibility of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing ML models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of ML and molecular featurization tools.

Simulation of lipid bilayer self-assembly using all-atom lipid force fields.

In this manuscript we expand significantly on our earlier communication by investigating the bilayer self-assembly of eight different types of phospholipids in unbiased molecular dynamics (MD) simulations using three widely used all-atom lipid force fields. Irrespective of the underlying force field, the lipids are shown to spontaneously form stable lamellar bilayer structures within 1 microsecond, the majority of which display properties in satisfactory agreement with the experimental data. The lipids self-assemble via the same general mechanism, though at formation rates that differ both between lipid types, force fields and even repeats on the same lipid/force field combination.

A Parameterization of Cholesterol for Mixed Lipid Bilayer Simulation within the Amber Lipid14 Force Field.

The Amber Lipid14 force field is expanded to include cholesterol parameters for all-atom cholesterol and lipid bilayer molecular dynamics simulations. The General Amber and Lipid14 force fields are used as a basis for assigning atom types and basic parameters. A new RESP charge derivation for cholesterol is presented, and tail parameters are adapted from Lipid14 alkane tails.

All-atom lipid bilayer self-assembly with the AMBER and CHARMM lipid force fields.

This communication reports the first example of spontaneous lipid bilayer formation in unbiased all-atom molecular dynamics (MD) simulations. Using two different lipid force fields we show simulations started from random mixtures of lipids and water in which four different types of phospholipids self-assemble into organized bilayers in under 1 microsecond.

Lipid14: The Amber Lipid Force Field.

The AMBER lipid force field has been updated to create Lipid14, allowing tensionless simulation of a number of lipid types with the AMBER MD package. The modular nature of this force field allows numerous combinations of head and tail groups to create different lipid types, enabling the easy insertion of new lipid species. The Lennard-Jones and torsion parameters of both the head and tail groups have been revised and updated partial charges calculated.

LIPID11: a modular framework for lipid simulations using amber.

Accurate simulation of complex lipid bilayers has long been a goal in condensed phase molecular dynamics (MD). Structure and function of membrane-bound proteins are highly dependent on the lipid bilayer environment and are challenging to study through experimental methods. Within Amber, there has been limited focus on lipid simulations, although some success has been seen with the use of the General Amber Force Field (GAFF).