PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities

Role and Regulation of Osmolytes and ABA Interaction in Salt and Drought Stress Tolerance

Abiotic stress conditions lead to the defects in plant growth and development and also reduction in flowering and fertility. Under prolonged stresses, imminent death of the plants has been observed. To cope with such stress conditions, plants accumulate a wide variety of organic solutes called osmolytes. Osmolytes are accumulated in bacteria, lower, and higher plants as a response primarily to abiotic stress. They encompass amino acids such as proline, tertiary sulfonium, and quaternary ammonium compounds like beatines, sugars (trehalose), and polyhydric alcohols (mannitol, sorbitol, pinitol, etc.). Osmolytes are accumulated in the cytoplasm as well as in chloroplasts in certain cases for osmotic adjustment under stress conditions. This enables the plants to absorb water and survive under stress. Out of the many phytohormones that play diverse roles during abiotic stress, abscisic acid (ABA) is an important one and perceived by plants by a core signaling module. As an integral part of signal transduction during stress conditions, ABA and other hormones regulate not only stomatal closure, but also a wide array of gene expressions including osmolyte biosynthetic pathway genes. Many signal molecules like nitric oxide, carbon monoxide, and hydrogen sulfide also play a vital role in osmolyte biosynthesis. Osmolytes appear to have multiple functions during stress such as osmotic adjustment and scavenging of reactive oxygen species (ROS). Thus, generation of ROS and osmolyte accumulation are linked together. This review summarizes the role played by ABA in signal transduction, the role of hormones to regulate osmolyte biosynthesis, and various functions carried out by them

PHYTOCHEMICAL ANALYSIS, LIQUID CHROMATOGRAPHY, AND MASS SPECTROSCOPY AND IN VITRO ANTICANCER ACTIVITY OF ANNONA SQUAMOSA SEEDS LINN.

 Objective: The objective of the present study is to evaluate in vitro anticancer property and phytochemical analysis using liquid chromatography and mass spectroscopy (LCMS) method of hydroalcoholic extract of seeds of Annona squamosa (AS) Linn. Seeds of AS Linn. are traditional medicine treating various diseases and have shown anticancer activity. Due to lack of survival benefit, cancer is a deadly global disease.Method: The anticancer activity was evaluated using the sulforhodamine B assay method on five cancer cell lines: Breast cancer cell line, cervix cancer cell line (SiHa), colon cancer cell line (HT)-29, liver cancer cell line, and ovary cancer cell line (Ovcar). The phytochemical analysis was performed using LCMS method.Result: The phytochemical characterization was done using LCMS method which showed 15 different molecular weight compounds. The extract showed an average in vitro anticancer activity at a concentration of 100 μg/ml against all cancer cell lines. The best activity was observed against Ovcar-5 cell line (69.72) and was also significant against HT and SiHa cell lines.Conclusion: The phytochemical analysis showed the wide range of phenols and flavonoid which are showing potent anticancer activity of AS seeds

PHYTOCHEMICAL ANALYSIS, LIQUID CHROMATOGRAPHY, AND MASS SPECTROSCOPY AND IN VITRO ANTICANCER ACTIVITY OF ANNONA SQUAMOSA SEEDS LINN.

 Objective: The objective of the present study is to evaluate in vitro anticancer property and phytochemical analysis using liquid chromatography and mass spectroscopy (LCMS) method of hydroalcoholic extract of seeds of Annona squamosa (AS) Linn. Seeds of AS Linn. are traditional medicine treating various diseases and have shown anticancer activity. Due to lack of survival benefit, cancer is a deadly global disease.Method: The anticancer activity was evaluated using the sulforhodamine B assay method on five cancer cell lines: Breast cancer cell line, cervix cancer cell line (SiHa), colon cancer cell line (HT)-29, liver cancer cell line, and ovary cancer cell line (Ovcar). The phytochemical analysis was performed using LCMS method.Result: The phytochemical characterization was done using LCMS method which showed 15 different molecular weight compounds. The extract showed an average in vitro anticancer activity at a concentration of 100 μg/ml against all cancer cell lines. The best activity was observed against Ovcar-5 cell line (69.72) and was also significant against HT and SiHa cell lines.Conclusion: The phytochemical analysis showed the wide range of phenols and flavonoid which are showing potent anticancer activity of AS seeds

MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement Learning

In this paper, we propose MolOpt, the first attempt of its kind to use Multi-Agent Reinforcement Learning (MARL) for autonomous molecular geometry optimization (MGO). Typically MGO algorithms are hand-designed, but MolOpt uses MARL to learn a learned optimizer (policy) that can perform MGO without depending on other hand-designed optimizers. We cast MGO as a MARL problem, where each agent corresponds to a single atom in the molecule. MolOpt performs MGO by minimizing the forces on each atom in the molecule. Our experiments demonstrate the generalizing ability of MolOpt for MGO of Propane, Pentane, Heptane, Hexane, and Octane when trained on Ethane, Butane, and Isobutane. In terms of performance, MolOpt outperforms the MDMin optimizer and demonstrates similar performance to the FIRE optimizer. However, it does not surpass the BFGS optimizer. The results demonstrate that MolOpt has the potential to introduce innovative advancements in MGO by providing a novel approach using reinforcement learning (RL), which may open up new research directions for MGO. Overall, this work serves as a proof-of-concept for the potential of MARL in MGO

Spectra to Structure: Deep Reinforcement Learning for Molecular Inverse Problem

Spectroscopy is the study of how matter interacts with electromagnetic radiations of specific frequencies that has led to several monumental discoveries in science. The spectra of any particular molecule is highly information-rich, yet the inverse relation from the spectra to the molecular structure is still an unsolved problem. Nuclear Magnetic Resonance (NMR) spectroscopy is one such critical tool in the tool-set for scientists to characterise any chemical sample. In this work, a novel framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure that resulted in the target spectra. The proposed framework uses a combination of online Monte- Carlo-Tree-Search (MCTS) and a set of offline trained Graph Convolution Networks to build a molecule iteratively from scratch. Our method is able to predict the correct structure of the molecule ∼80% of the time in its top 3 guesses. We believe that the proposed framework is a significant step in solving the inverse design problem of NMR spectra to molecule

Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-like Molecules

Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents within the limits of chemical accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.</div

Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules

Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents with high accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable

Data_Sheet_1_MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization.PDF

The pursuit of potential inhibitors for novel targets has become a very important problem especially over the last 2 years with the world in the midst of the COVID-19 pandemic. This entails performing high throughput screening exercises on drug libraries to identify potential “hits”. These hits are identified using analysis of their physical properties like binding affinity to the target receptor, octanol-water partition coefficient (LogP) and more. However, drug libraries can be extremely large and it is infeasible to calculate and analyze the physical properties for each of those molecules within acceptable time and moreover, each molecule must possess a multitude of properties apart from just the binding affinity. To address this problem, in this study, we propose an extension to the Machine learning framework for Enhanced MolEcular Screening (MEMES) framework for multi-objective Bayesian optimization. This approach is capable of identifying over 90% of the most desirable molecules with respect to all required properties while explicitly calculating the values of each of those properties on only 6% of the entire drug library. This framework would provide an immense boost in identifying potential hits that possess all properties required for a drug molecules.</p