AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Understanding Homogeneous and Heterogeneous Transition Metal Oxide Catalytic Systems for Partial Oxidation Reactions

Transition metal oxide-based systems as catalysts for oxidative reactions are studied. Utilizing aerobic oxygen as a cost-efficient and benign oxidant is explored for alcohol oxidation reactions. A detailed magnetic study is performed to understand the active phases of manganese oxide catalysts. The role of a well-known homogeneous copper oxide catalyst in an oxidation reaction is explored for making alcohols from aliphatic hydrocarbons. Finally, virtual screening techniques are explored in a preliminary study for materials discovery. In chapter I, aerobic transformation of alcohols is studied by mesoporous manganese oxide and cobalt oxide species. Catalyst optimization, characterization and substrate scope experimentation is performed for self-esterification of alcohols and aldehydes. In chapter II, a comprehensive magnetic characterization of mesoporous manganese oxide species is provided. While conventional methods do not lead to definitive phase determination, magnetic transitions and spin eco NMR techniques prove the mixed phase of meso MnOx materials. In chapter III, a synthetic copper complex is studied. The complex was modeled after the active site of particulate methane monooxygenase (pMMO). A detailed study of the complex structure, reaction medium and by- products showed that an important side reaction interfered with hydrocarbon oxidation. The nitrile solvent decomposes to amide by nucleophilic interactions from hydrogen peroxide. In the final chapter, preliminary computational screening tools are introduced. Extraction of geometrical features of molecular structures and evaluation of total energy with machine learning algorithms are performed and the ground work for application of such work to crystalline materials is proposed

Melatonin effects on sleep disorders in children with attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder is one of the most common psychiatric disorders in childhood. Around 25-50% of these children suffered from some kind of sleep disorder especially with chronic form of insomnia. The physicians usually have a plan for improving hyperactivity and attention deficit of this disease but unfortunately, they forget to manage the sleep disorders, which are a major part of patients’ problems.Nowadays, we know that there is a noticeable relationship between attention deficit hyperactivity disorder and sleep disorders and by improving these children's sleep, not only the daily functions improve, but also the symptoms of attention deficit hyperactivity disorder  maybe become better. Thus, it is needed to avoid the administration of psychostimulants, which have recognized side effects. Moreover, having better sleep, we will see a better relationship between children and their parents and finally a rise in the standard of life of family members, which is a very important goal in our treatment. This review article evaluates available evidence on sleep medication in children with attention deficit hyperactivity disorder to present an appropriate guidance for this high prevalence problem

Photocatalytic Water Splitting—The Untamed Dream: A Review of Recent Advances

Photocatalytic water splitting using sunlight is a promising technology capable of providing high energy yield without pollutant byproducts. Herein, we review various aspects of this technology including chemical reactions, physiochemical conditions and photocatalyst types such as metal oxides, sulfides, nitrides, nanocomposites, and doped materials followed by recent advances in computational modeling of photoactive materials. As the best-known catalyst for photocatalytic hydrogen and oxygen evolution, TiO2 is discussed in a separate section, along with its challenges such as the wide band gap, large overpotential for hydrogen evolution, and rapid recombination of produced electron-hole pairs. Various approaches are addressed to overcome these shortcomings, such as doping with different elements, heterojunction catalysts, noble metal deposition, and surface modification. Development of a photocatalytic corrosion resistant, visible light absorbing, defect-tuned material with small particle size is the key to complete the sunlight to hydrogen cycle efficiently. Computational studies have opened new avenues to understand and predict the electronic density of states and band structure of advanced materials and could pave the way for the rational design of efficient photocatalysts for water splitting. Future directions are focused on developing innovative junction architectures, novel synthesis methods and optimizing the existing active materials to enhance charge transfer, visible light absorption, reducing the gas evolution overpotential and maintaining chemical and physical stability

Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)

Using molecular simulation for adsorbent screening is computationally expensive and thus prohibitive to materials discovery. Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods for screening purposes. Prior efforts have focused on structural descriptors for use with ML. In this work, the use of chemical descriptors, in addition to structural descriptors, was introduced for adsorption analysis. Evaluation of structural and chemical descriptors coupled with various ML algorithms, including decision tree, Poisson regression, support vector machine and random forest, were carried out to predict methane uptake on hypothetical metal organic frameworks. To highlight their predictive capabilities, ML models were trained on 8% of a data set consisting of 130,398 MOFs and then tested on the remaining 92% to predict methane adsorption capacities. When structural and chemical descriptors were jointly used as ML input, the random forest model with 10-fold cross validation proved to be superior to the other ML approaches, with an <i>R</i>² of 0.98 and a mean absolute percent error of about 7%. The training and prediction using the random forest algorithm for adsorption capacity estimation of all 130,398 MOFs took approximately 2 h on a single personal computer, several orders of magnitude faster than actual molecular simulations on high-performance computing clusters

Improved Understanding of CO₂–Water Pretreatment of Guayule Biomass by High Solids Ratio Experiments, Rapid Physical Expansion, and Examination of Textural Properties

In this work, we provide a systematic study of CO₂–water pretreatment of guayule biomass to optimize the residual ground bagasse from natural rubber extraction for hydrolysis and fermentation. Guayule biomass is mixed with water then loaded into a 250 mL reactor with exposure to a biphasic environment consisting of a CO₂-rich vapor phase and water-rich liquid phase. The pressure is then rapidly released for a “physical expansion” effect. The pretreated biomass is enzymatically hydrolyzed, and the sugar concentration in hydrolysate is measured. Experimental runs are conducted in the temperature range of 145 to 210 °C and pressure range of 3.4 to 34 MPa. The solids ratio (dry solids mass/water mass) is between 0.17 and 1.7. The packing density is between 0.03 and 0.2 g of biomass per cm³ of the reactor, and the holding time ranges from 20 to 840 min. A 4-fold increase in the reactor volume is performed and optimized with a 1-L vessel. High-pressure carbon dioxide–water pretreatment increases surface area of guayule biomass, introduces ruptured morphological features, and improves enzymatic digestibility. We achieve a total sugar yield of 85% (of theoretical) at two different reactor sizes of 250 mL and 1 L. At the smaller reactor, the optimum operational condition is 180 °C, 26 MPa, and 0.5 solid ratio. At the larger reactor, the optimum operational condition is 200 °C, 12 MPa, and 0.33 solid ratio

Synthesis of Large Mesoporous–Macroporous and High Pore Volume, Mixed Crystallographic Phase Manganese Oxide, Mn₂O₃/Mn₃O₄ Sponge

The controlled synthesis of mixed crystallographic phase Mn₂O₃/Mn₃O₄ sponge material by varying heating rates and isothermal segments provides valuable information about the morphological and physical properties of the obtained sample. The well-characterized Mn₂O₃/Mn₃O₄ sponge and applicability of difference in reactivity of H₂ and CO₂ desorbed during the synthesis provide new developments in the synthesis of metal oxide materials with unique morphological and surface properties. We report the preparation of a Mn₂O₃/Mn₃O₄ sponge using a metal nitrate salt, water, and Dextran, a biopolymer consisting of glucose monomers. The Mn₂O₃/Mn₃O₄ sponge prepared at 1 °C·min^–1 heating rate to 500 °C and held isothermally for 1 h consisted of large mesopores–macropores (25.5 nm, pore diameter) and a pore volume of 0.413 mL/g. Furthermore, the prepared Mn₂O₃/Mn₃O₄ and 5 mol %–Fe-Mn₂O₃/Mn₃O₄ sponges provide potential avenues in the development of solid-state catalyst materials for alcohol and amine oxidation reactions