Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Development of a computational approach to predict blood-brain barrier permeability. Drug Metab Dispos 32:132–139.

This article is available online at http://dmd.aspetjournals.org ABSTRACT: The objectives of this study were to generate a data set of bloodbrain barrier (BBB) permeability values for drug-like compounds and to develop a computational model to predict BBB permeability from structure. The BBB permeability, expressed as permeabilitysurface area product (PS, quantified as logPS), was determined for 28 structurally diverse drug-like compounds using the in situ rat brain perfusion technique. A linear model containing three descriptors, logD, van der Waals surface area of basic atoms, and polar surface area, was developed based on 23 compounds in our data set, where the penetration across the BBB was assumed to occur primarily by passive diffusion The blood-brain barrier (BBB 1 ) consists of a continuous layer of endothelial cells joined by tight junctions at the cerebral vasculature. It represents a physical and enzymatic barrier to restrict and regulate the penetration of compounds into and out of the brain and maintain the homeostasis of the brain microenvironmen

TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics

TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics Brajesh K. Rai*,1, Vishnu Sresht1, Qingyi Yang2, Ray Unwalla2, Meihua Tu2, Alan M. Mathiowetz2, and Gregory A. Bakken3 1Simulation and Modeling Sciences and 2Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States 3Digital, Pfizer, Eastern Point Road, Groton, Connecticut 06340, United States ABSTRACT Fast and accurate assessment of small molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains a challenging task as current molecular mechanics methods are limited by insufficient coverage of druglike chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate library and leveraged massively parallel cloud computing resources to perform DFT torsion scan of these fragments, generating a training dataset of 1.2 million DFT energies. By training TorsionNet on this dataset, we obtain a model that can rapidly predict the torsion energy profile of typical druglike fragments with DFT-level accuracy. Importantly, our method also provides a direct estimate of the uncertainty in the predicted profiles without any additional calculations. In this report, we show that TorsionNet can reliably identify the preferred dihedral geometries observed in crystal structures. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. A benchmark dataset (TorsionNet500) comprising 500 chemically diverse fragments with DFT torsion profiles (12k DFT-optimized geometries and energies) has been created and is made freely available.</p

Exploring Aromatic Chemical Space with NEAT: Novel and Electronically Equivalent Aromatic Template

In this paper, we describe a lead transformation tool, NEAT (<u>N</u>ovel and <u>E</u>lectronically equivalent <u>A</u>romatic <u>T</u>emplate), which can help identify novel aromatic rings that are estimated to have similar electrostatic potentials, dipoles, and hydrogen bonding capabilities to a query template; hence, they may offer similar bioactivity profiles. In this work, we built a comprehensive heteroaryl database, and precalculated high-level quantum mechanical (QM) properties, including electrostatic potential charges, hydrogen bonding ability, dipole moments, chemical reactivity, and othe properties. NEAT bioisosteric similarities are based on the electrostatic potential surface calculated by Brood, using the precalculated QM ESP charges and other QM properties. Compared with existing commercial lead transformation software, (1) NEAT is the only one that covers the comprehensive heteroaryl chemical space, and (2) NEAT offers a better characterization of novel aryl cores by using high-evel QM properties that are relevant to molecular interactions. NEAT provides unique value to medicinal chemists quickly exploring the largely uncharted aromatic chemical space, and one successful example of its application is discussed herein