Since the availability of experimentally determined structures, such as X-ray crystallography data from the Protein Data Bank (PDB), computational scientists have essentially been required to use these structures as initial models for simulations. The process of creating model structures varies widely among researchers, ranging from open source to commercial software, aimed at achieving physically and chemically accurate models. However, with multiple structures in the PDB, there is no clear guideline for selecting the optimal one. This article summarizes, based on the author's experience, five key points to consider when choosing structures for simulations like molecular docking, molecular dynamics, and fragment molecular orbital (FMO) calculations.

The fragment molecular orbital (FMO) program ABINIT-MP has a quarter-century history, and related research and development of the Open Version 2 series is currently underway. This paper first summarizes the current status of the latest Revision 8 (released on August 2023). It then describes future improvements and enhancements, including GPU support. The connection with coarse-grained simulation (dissipative particle dynamics) and the possibility of cooperation with quantum computation are also touched upon.

Abstract: Fragment Molecular Orbital (FMO) is a unique method that allows quantum mechanical (QM) calculations of entire proteins. The data obtained by the FMO method are also currently the only QM calculation data for protein systems. The development of various machine learning models using the QM calculation data of proteins, which are difficult to generate with general-purpose software, is expected to have a significant impact on AI drug discovery, which has been remarkably active in recent years. This paper outlines the status of the development of machine learning models using FMO data, which is ongoing in the author's group.

Recent efforts have focused on utilizing molecular interaction data from FMO calculations for phase separation simulations in materials design. Accurate prediction of phase separation, which is closely related to molecular affinity, has long been a challenge due to difficulties in calculating accurate interaction parameters. We have developed a framework for estimating effective interaction parameters between coarse-grained components using FMO calculations. In addition, a simulation scheme called FMO-DPD, which applies these parameters to dissipative particle dynamics (DPD) simulations, has demonstrated its effectiveness in various systems. These developments are discussed in this paper.

While Amorphous solid dispersion is an effective method for improving the solubility of pharmaceutical formulations, it presents stability challenges, as moisture absorption can accelerate crystallization. In this study, we employed a model system using four drug molecules (Droperidol, Nifedipine, Indomethacin, Ketoprofen) and the carrier polymer Polyvinylpyrrolidone, where molecular distribution was simulated using MD methods, and interaction energies were calculated using the FMO method. We analyzed the changes in interaction energies due to moisture absorption, clarifying the differences in crystallization tendencies and stability of the drugs.