Machine Learning and Molecular Dynamics Based Insights into Mode of Actions of Insulin Degrading Enzyme Modulators

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Brine-Oil Interfacial Tension Modeling: Assessment of Machine Learning Techniques Combined with Molecular Dynamics

The physical chemistry mechanisms behind the oil-brine interface phenomena are not yet fully clarified. The knowledge of the relation between brine composition and concentration for a given oil may lead to the ionic tuning of the injected solution on geochemical and enhanced oil recovery processes. Thus, it is worth examining the parameters influencing the interfacial properties. In this context, we have combined machine learning (ML) techniques with classical molecular dynamics simulations (MD) to predict oil/brine interfacial tensions (IFT) effectively and compared this process to a linear regression (LR) method. To diversify our data set, we have introduced a new atomistic crude oil model (medium) with 36 different types of hydrocarbon molecules. The MD simulations were performed for mono- and multicomponent (toluene, heptane, Heptol, light, and medium) oil systems interfaced with sulfate and chloride brines with varying cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺) and salinity concentration. Thus, a consistent IFT data set was built for the ML training and LR fitting at room temperature and pressure conditions, over the feature space considering oil density, oil composition, salinity, and ionic concentrations. On the basis of gradient boosted (GB) algorithms, we have observed that the dominant quantities affecting the IFT are related to the oil attributes and the salinity concentration, and no specific ion dominates the IFT changes. When the obtained LR model was validated against MD and experimental data from the literature, the error varied up to 2% and 9%, respectively, showing a robust and consistent transferability. The combination of MD simulations and ML techniques may provide a fast and cost-effective IFT determination over multiple and complex fluid-fluid and fluid-solid interfaces.

Machine Learning From Molecular Dynamics Trajectories to Predict Caspase-8 Inhibitors Against Alzheimer's Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells takes place leading to loss of memory and decreased cognitive ability. AD is a leading cause of death worldwide and is progressive in nature with symptoms worsening over time. Machine learning–based computational predictive models based on 2D and 3D descriptors have been effective in identifying potential active compounds. However, the use of data from molecular dynamics (MD) trajectories for training machine learning models still needs to be explored. In the present study, descriptors have been extracted from the MD trajectories of caspase-8 ligand complexes to train models using artificial neural networks and random forest algorithms. Caspase-8 plays a key role in causing AD by cleaving amyloid precursor proteins during apoptosis leading to increased formation of the amyloid-beta peptide. A total of 43 ligands were docked using the glide module of Schrodinger software, and short MD simulations of 10 ns were performed for the calculation of MD descriptors. The MD descriptors were also combined with the 2D and 3D descriptors of chemical compounds, and individual descriptor based as well as combination models were generated. This study demonstrated that MD descriptors could be effectively used for the characterization of bioactive compounds along with lead prioritization and optimization.