Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning.

Deep convolutional neural networks (CNNs) are a method of choice for image recognition. Herein a hybrid CNN approach is presented for molecular pattern recognition in drug discovery. Using self-organizing map images of molecular pharmacophores as input, CNN models were trained to identify chemokine receptor CXCR4 modulators with high accuracy. This machine learning classifier identified first-in-class synthetic CXCR4 full agonists. The receptor-activating effects were confirmed by intracellular cAMP response and in a phenotypic spheroid invasion assay of medulloblastoma cell invasion. Additional macromolecular targets of the small molecules were predicted in silico and tested in vitro, revealing modulatory effects on dopamine receptors and CCR1. These results positively advocate the applicability of molecular image recognition by CNNs to ligand-based virtual compound screening, and demonstrate the complementarity of machine intelligence and human expert knowledge.

Deep Learning in Drug Discovery.

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.

Simulated Solute Tempering in Fully Polarizable Hybrid QM/MM Molecular Dynamics Simulations.

We successfully apply a solute tempering approach, which substantially reduces the large number of temperature rungs required in conventional tempering methods by solvent charge scaling, to hybrid molecular dynamics simulations combining quantum mechanics with molecular mechanics (QM/MM). Specifically, we integrate a combination of density functional theory (DFT) and polarizable MM (PMM) force fields into the simulated solute tempering (SST) concept. We show that the required DFT/PMM-SST weight parameters can be obtained from inexpensive calculations and that for alanine dipeptide (DFT) in PMM water three rungs suffice to cover the temperature range from 300 to 550 K.