Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water.

Molecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, ab initio molecular dynamics (AIMD) has the accuracy of density functional theory (DFT) and thus is limited to small systems and a relatively short simulation time. In this scenario, Neural Network Force Fields (NNFFs) have an important role, since they provide a way to circumvent these caveats. In this work, we investigate NNFFs designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data set considered. We show that structural properties are less dependent on the size of the training data set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for the training process) can lead to a small sample with good precision.

Revealing Temozolomide Resistance Mechanisms via Genome-Wide CRISPR Libraries.

Glioblastoma is a severe type of brain tumor with a poor prognosis and few therapy options. Temozolomide (TMZ) is one of these options, however, with limited success, and failure is mainly due to tumor resistance. In this work, genome-wide CRISPR-Cas9 lentiviral screen libraries for gene knockout or activation were transduced in the human glioblastoma cell line, aiming to identify genes that modulate TMZ resistance. The sgRNAs enriched in both libraries in surviving cells after TMZ treatment were identified by next-generation sequencing (NGS). Pathway analyses of gene candidates on knockout screening revealed several enriched pathways, including the mismatch repair and the Sonic Hedgehog pathways. Silencing three genes ranked on the top 10 list (MSH2, PTCH2, and CLCA2) confirm cell protection from TMZ-induced death. In addition, a CRISPR activation library revealed that NRF2 and Wnt pathways are involved in TMZ resistance. Consistently, overexpression of FZD6, CTNNB1, or NRF2 genes significantly increased cell survival upon TMZ treatment. Moreover, NRF2 and related genes detected in this screen presented a robust negative correlation with glioblastoma patient survival rates. Finally, several gene candidates from knockout or activation screening are targetable by inhibitors or small molecules, and some of them have already been used in the clinic.

Ab Initio Simulations and Materials Chemistry in the Age of Big Data.

In this perspective, we discuss computational advances in the last decades, both in algorithms as well as in technologies, that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems. Such advances led to the generation of large amounts of data, which required the creation of several computational databases. Within this scenario, with the democratization of data access, the field now encounters several opportunities for data-driven approaches toward chemical and materials problems. Specifically, machine learning methods for predictions of novel materials or properties are being increasingly used with great success. However, black box usage fails in many instances; several technical details require expert knowledge in order for the predictions to be useful, such as with descriptors and algorithm selection. These approaches represent a direction for further developments, notably allowing advances for both developed and emerging countries with modest computational infrastructures.

Simulating DNA Chip Design Using All-Electronic Graphene-Based Substrates.

In this paper, we present a theoretical investigation of an all-electronic biochip based on graphene to detect DNA including a full dynamical treatment for the environment. Our proposed device design is based on the changes in the electronic transport properties of graphene interacting with DNA strands under the effect of the solvent. To investigate these systems, we applied a hybrid methodology, combining quantum and classical mechanics (QM/MM) coupled to non-equilibrium Green's functions, allowing for the calculations of electronic transport. Our results show that the proposed device has high sensitivity towards the presence of DNA, and, combined with the presence of a specific DNA probe in the form of a single-strand, it presents good selectivity towards specific nucleotide sequences.