Influence of Assimilation Effects on Recommender Systems

Recommender systems are a common approach in retail e-commerce to support consumers in finding relevant products. Not surprisingly, user acceptance of personalized product recommendations tends to be higher, leading to higher click rates. Since contextual information also influences user search behavior, we analyze the importance of similarity between recommendations and the underlying context a currently inspected product provides. Using data from a midsize European retail company, we conduct a field experiment and investigate the role of similarities between focal product information and recommendations from a collaborative filtering algorithm. We find that contextual similarity, primarily visual similarity contributes much explanation to consumer click behavior, underlining the importance of contextual and content information in the recommender system\u27s environment

Improving Protein-peptide Interface Predictions in the Low Data Regime

We propose a novel approach for predicting protein-peptide interactions using a bi-modal transformer architecture that learns an inter-facial joint distribution of residual contacts. The current data sets for crystallized protein-peptide complexes are limited, making it difficult to accurately predict interactions between proteins and peptides. To address this issue, we propose augmenting the existing data from PepBDB with pseudo protein-peptide complexes derived from the PDB. The augmented data set acts as a method to transfer physics-based contextdependent intra-residue (within a domain) interactions to the inter-residual (between) domains. We show that the distributions of inter-facial residue-residue interactions share overlap with inter residue-residue interactions, enough to increase predictive power of our bi-modal transformer architecture. In addition, this dataaugmentation allows us to leverage the vast amount of protein-only data available in the PDB to train neural networks, in contrast to template-based modeling that acts as a priorComment: 5 pages, 5 figures, ICLR Machine Learning in Drug Discovery Accepted pape

Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes

Understanding the macroscopic characteristics of biological complexes demands precision and specificity in statistical ensemble modeling. One of the primary challenges in this domain lies in sampling from particular subsets of the state-space, driven either by existing structural knowledge or specific areas of interest within the state-space. We propose a method that enables sampling from distributions that rigorously adhere to arbitrary sets of geometric constraints in Euclidean spaces. This is achieved by integrating a constraint projection operator within the well-regarded architecture of Denoising Diffusion Probabilistic Models, a framework founded in generative modeling and probabilistic inference. The significance of this work becomes apparent, for instance, in the context of deep learning-based drug design, where it is imperative to maintain specific molecular profile interactions to realize the desired therapeutic outcomes and guarantee safety.Comment: Published at ICML 2023 Workshop on Structured Probabilistic Inference and Generative Modelin

Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix

Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matrices between ligand atoms and protein C_alpha atoms thus incorporating side-chain flexibility implicitly

PharmDock: A Pharmacophore-Based Docking Program

Background Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function. Results Tests of PharmDock on ligand pose prediction, binding affinity estimation, compound ranking and virtual screening yielded comparable or better performance to existing and widely used docking programs. The docking program comes with an easy-to-use GUI within PyMOL. Two features have been incorporated in the program suite that allow for user-defined guidance of the docking process based on previous experimental data. Docking with those features demonstrated superior performance compared to unbiased docking. Conclusion A protein pharmacophore-based docking program, PharmDock, has been made available with a PyMOL plugin. PharmDock and the PyMOL plugin are freely available fromhttp://people.pharmacy.purdue.edu/~mlill/software/pharmdock webcite

IterTunnel; a Method for Predicting and Evaluating Ligand EgressTunnels in Proteins with Buried Active Sites

Poster Presentation: The computational prediction of ligand entry and egress paths in proteins has become an emerging topic in computational biology due to the potential for estimating kinetic properties of drug binding. These properties are related to important pharmacological quantities such as the kon and koff rate of drugs [1,2].We have investigated the influence of protein flexibility on tunnel prediction using geometric methods by comparing tunnels identified in static structures with those found in structural ensembles of three CYP isozymes. We found drastic differences between tunnels predicted in the crystal structures as opposed to those predicted in the ensembles [3]. Furthermore, we found significant differences between tunnels identified in the apo versus the holo protein ensembles [3]. While geometric prediction provides a good starting point for tunnel prediction, in order to estimate kinetic properties, more detailed investigations of the ligand binding process are required. We have developed a tunnel prediction methodology, IterTunnel, which predicts tunnels in proteins and estimates the free energy of ligand unbinding using a combination of geometric tunnel prediction with steered molecular dynamics and umbrella sampling [4]. Applying this new method to cytochrome P450 2B6 (CYP2B6), we demonstrate that the ligand itself plays an important role in reshaping tunnels as it traverses through a protein. This process results in the exposure of new tunnels and the closure of pre-existing tunnels as the ligand migrates from the active site. We found that many of the tunnels that are exposed due to ligand-induced conformational changes are amongst the most energetically favorable tunnels for ligand egress in CYP2B6 [4]

Metrics for measuring distances in configuration spaces

In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. We show that these metrics correlate well with the RMSD between two configurations if this RMSD is obtained from a global minimization over all translations, rotations and permutations of atomic indices. We introduce a Monte Carlo approach to obtain this global minimum of the RMSD between configurations