Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks

Due to diverse reasons, most drug candidates cannot eventually become marketed drugs. Developing reliable computational methods for prediction of drug-likeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used a fully connected neural networks (FNN) to construct drug-likeness classification models with deep autoencoder to initialize model parameters. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of drug-like/non-drug-like model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-drug-like model (ZINC World Drug vs. ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art drug-likeness prediction models

Theory and Modeling of Molecular Motion out of Equilibrium

Molecules at temperatures above 0K are always in motion, translating, rotating, and undergoing conformational changes. In systems that are out of equilibrium, these motions often become more intense and complex, leading to interesting phenomena, including the existence of life. This dissertation presents theoretical and computational modeling for some of these phenomena. First, many enzymes appear to diffuse faster in the presence of their substrates and to drift along concentration gradients of their substrate, phenomena known respectively as enhanced enzyme diffusion and enzyme chemotaxis. Here, experimental findings and proposed mechanisms for these observations are critically reviewed, then we propose a kinematic and thermodynamic analysis to serve as a validity check for any mechanism that attributes enhanced enzyme diffusion to self-propulsion. Second, overcrowded alkene-based molecular motors, a class of synthetic small molecules designed for light-driven rotation of its rotor part relative to its stator part, exhibit fast rotation in the microsecond timescale. Here, the full rotation process is modeled by quantum surface-hopping molecular dynamics simulations coupled with classical molecular dynamics simulations. This study proposes a novel rotation pathway, as well as providing computational predictions for rotation rate and maximal power output. Encouraging agreement with experiments are found, after fitting critical forcefield parameters to reference quantum mechanical energy surfaces. In conclusion, these efforts contribute to better understanding of molecular motions out of equilibrium and how to conceptualize and model them

A Thermodynamic Limit on the Role of Self-Propulsion in Enhanced Enzyme Diffusion

A number of enzymes reportedly exhibit enhanced diffusion in the presence of their substrates, with a Michaelis-Menten-like concentration dependence. Although no definite explanation of this phenomenon has emerged, a physical picture of enzyme self-propulsion using energy from the catalyzed reaction has been widely considered. Here, we present a kinematic and thermodynamic analysis of enzyme self-propulsion that is independent of any specific propulsion mechanism. Using this theory, along with biophysical data compiled for all enzymes so far shown to undergo enhanced diffusion, we show that the propulsion speed required to generate experimental levels of enhanced diffusion exceeds the speeds of well-known active biomolecules, such as myosin, by several orders of magnitude. Furthermore, the minimal power dissipation required to account for enzyme enhanced diffusion by self-propulsion markedly exceeds the chemical power available from enzyme-catalyzed reactions. Alternative explanations for the observation of enhanced enzyme diffusion therefore merit stronger consideration

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening.

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process