Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

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We have studied the structure of aggregates formed by oppositely charged polyelectrolytes of the same chain length N and charge density and the kinetics of the formation of these aggregates using a dynamic Monte Carlo algorithm. The model considers the evolution of chains through local motion of monomers and exhibits the complex behavior associated with the formation of such aggregates. The chains interpenetrate in two distinct steps-the chains first diffuse toward each other and then collapse abruptly to form smaller aggregates. This abrupt collapse is a consequence of cooperativity in the aggregation process. The chains collapse and are considerably smaller in the aggregate than in isolation; the conformational properties of each of the two chains of the same chain length and charge density in the aggregate are the same. The radii of gyration of the chains in the aggregate and of the aggregate scale as Wla. Aggregates formed by longer chains initially scale as N. These aggregates continue to reorganize and eventually scale as P I z . However, it is possible that for sufficiently long chains, as in real experimental situations, this process of reorganization is hindered by the formation of entanglements, etc. The aggregates would then form a nonequilibrium structure and would not scale as IWZ. I. IntroductionSpecific interactions such as hydrogen-bonding and electrostatic interactions in macromolecules result in the formation of supramolecular structures in many applications such as ionomers and gels and exercise great influence on material behavior.14 These structures form spontaneously and are characterized by a wide range of organization. The extent of organization is determined by molecular architecture and the strength of intermolecular interactions. Also, intermolecular interactions mediate molecular recognition in biological systems.Electrostatic interactions are the strongest among molecular interactions and have been studied extensively experimentally. Although the behavior of uniformly charged polyelectrolytes in solution is well understood, a molecular understanding of the formation of aggregates by oppositely charged polyelectrolytes and of the kinetics of the formation of these aggregates is lacking at present. As a model system for interaction between chains we consider the interaction of two oppositely charged chains of the same chain length and charge density.Baljon-Haakman and Wittens have recently studied the structure of two associating chains which are constrained to adhere to each other by stickers. Each chain, in this study, has two stickers located along its contour. A sticker can adhere to another sticker located on the same chain or to one located on the other chain. The stickers are free to exchange partners. This freedom to exchange partners leads to an increase in the number of possible conformations and amounts to an entropic attraction. This attractive interaction is offset by the repulsive interaction between two self-paired chains in a good solvent. Thus alteration of sticker plac...

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Neuropathy target esterase (NTE) is a membrane protein found in human neurons and other cells, including lymphocytes. Binding of certain organophosphorus (OP) compounds to NTE is believed to cause OP-induced delayed neuropathy (OPIDN), a type of paralysis for which there is no effective treatment. Mutations in NTE have also been linked with serious neurological diseases, such as motor neuron disease. This paper describes development of the first nanostructured biosensor interface containing a catalytically active fragment of NTE known as NEST. The biosensor was fabricated using the layer-by-layer assembly approach, by immobilizing a layer of NEST on top of multilayers consisting of a polyelectrolyte (poly-L-lysine) and an enzyme (tyrosinase). The biosensor has a response time on the order of seconds and gives a concentration-dependent decrease in sensor output in response to a known NEST (and NTE) inhibitor. Potential applications of the biosensor include screening OP compounds for NTE inhibition and investigating the enzymology of wild-type and mutant forms of NTE. Although the development of a NEST biosensor was the primary purpose of this study, we found that the approach developed for NEST could also be extended to measure the activity of other esterases involved in neural processes, such as acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). On the basis of measured sensitivities, phenyl valerate was the preferred substrate for NEST and BChE, whereas phenyl acetate was better for AChE.

Knowledge of the kinetics of the rod cyclic GMP phosphodiesterase is essential for understanding the kinetics and gain of the light response. Therefore, the interactions between Mg2+, cyclic GMP, and purified, trypsin-activated bovine rod cyclic GMP phosphodiesterase (EC 3.1.4.17) were examined. The effects of Mg2+ and of cyclic GMP on the rod phosphodiesterase activity were mutually concentration-dependent. Formation of a free Mg-cyclic GMP complex is unlikely due to its high dissociation constant (Kd = 19 mM). Plots of 1/velocity versus 1/[cyclic GMP] as a function of [Mg2+] and 1/velocity versus 1/[Mg2+] as a function of [cyclic GMP] intersected to the left of the 1/velocity axis. This is consistent with the formation of a ternary complex between the phosphodiesterase, Mg2+, and cyclic GMP. A competitive inhibitor of the phosphodiesterase relative to cyclic GMP, 3-isobutyl-1-methylxanthine, non-competitively inhibited the enzyme relative to Mg2+, Pb2+, a competitive inhibitor of the phosphodiesterase relative to Mg2+ [D. Srivastava, R.L. Hurwitz and D. A. Fox (1995) Toxicol. Appl. Pharmacol, in the press] non-competitively inhibited the enzyme relative to cyclic GMP. Collectively these results are suggestive of a rapid equilibrium random binding order of Mg2+ and cyclic GMP to the rod phosphodiesterase.

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