An Artificial Neural Network-Based Estimation of Bremsstarahlung Photon Flux Calculated by MCNPX

Bremsstrahlung has an important place in the field of experimental physics, especially for description of photon-matter interaction and for characterization and analysis of materials. Bremsstrahlung photon is created by a high-energy electron, deflected in the electric field of atomic nucleus. Bremsstrahlung is also important for experimental studies, not only in the field of nuclear physics and particle physics but also in the fields of solid state physics, applied physics and astrophysics. In recent years, Monte Carlo simulation has become a widely used method for calculations related to bremsstrahlung. On the other hand, predictions by using artificial neural network can be performed with high accuracy. This study aims at observing variation in the photon flux as unction of target thickness and at processing output data by using an artificial neural network. We achieved a high degree of compatibility between two different methods. This study suggests that artificial neural network is a powerful tool for prediction of Bremsstrahlung and for other scientific problems

Target-Independent Prediction of Drug Synergies Using Only Drug Lipophilicity

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms