Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge.

Manifold representations of rotational/translational motion and conformational space of a ligand were previously shown to be effective for local energy optimization. In this paper we report the development of the Monte-Carlo energy minimization approach (MCM), which uses the same manifold representation. The approach was integrated into the docking pipeline developed for the current round of D3R experiment, and according to D3R assessment produced high accuracy poses for Cathepsin S ligands.

ClusPro FMFT-SAXS: Ultra-fast Filtering Using Small-Angle X-ray Scattering Data in Protein Docking.

We have recently demonstrated that incorporation of small-angle X-ray scattering (SAXS)-based filtering in our heavily used docking server ClusPro improves docking results. However, the filtering step is time consuming, since ≈10 conformations have to be sequentially processed. At the same time, we have demonstrated the possibility of ultra-fast systematic energy evaluation for all rigid body orientations of two proteins, by sampling using Fast Manifold Fourier Transform (FMFT), if energies are represented as a combination of convolution-like expressions.

Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of in embryonic and cancer cells.

Here we present the application of deep neural network (DNN) ensembles trained on transcriptomic data to identify the novel markers associated with the mammalian embryonic-fetal transition (EFT). Molecular markers of this process could provide important insights into regulatory mechanisms of normal development, epimorphic tissue regeneration and cancer. Subsequent analysis of the most significant genes behind the DNNs classifier on an independent dataset of adult-derived and human embryonic stem cell (hESC)-derived progenitor cell lines led to the identification of gene as a potential EFT marker.

Pepsi-SAXS: an adaptive method for rapid and accurate computation of small-angle X-ray scattering profiles.

A new method called Pepsi-SAXS is presented that calculates small-angle X-ray scattering profiles from atomistic models. The method is based on the multipole expansion scheme and is significantly faster compared with other tested methods. In particular, using the Nyquist-Shannon-Kotelnikov sampling theorem, the multipole expansion order is adapted to the size of the model and the resolution of the experimental data.

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology.

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator.