Molecular dynamics simulations can now track rapid processes—those occurring in less than about a millisecond—at atomic resolution for many biologically relevant systems. These simulations appear poised to exert a significant impact on how new drugs are found, perhaps even transforming the very process of drug discovery. We predict here future results we can expect from, and enhancements we need to make in, molecular dynamics simulations over the coming 25 years, and in so doing set out several Grand Challenges for the field. In the context of the problems now facing the pharmaceutical industry, we ask how we can best address drug discovery needs of the next quarter century using molecular dynamics simulations, and we suggest some possible approaches.

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The future of molecular dynamics simulations in drug discovery.

The recent elucidation of some of the critical molecular events involved in the maintenance of the neoplastic phenotype has engendered a new realism in cancer research. Not only is there a more unified conceptual base for such hitherto disparate disciplines as cytogenetics, chemical carcinogenesis and viral oncology but the new information suggests novel and in many respects, more rational approaches to treatment. These developments are to be welcomed, not least because they will ultimately make the costly and inefficient screening procedures on which advances in cancer chemotherapy have so largely relied, less empirical and more objective. The appearance this month of a new quarterly journal Anti Cancer Drug Design (published by the Macmillan Press Ltd) is therefore desirable and timely. Its aims and scope which are complementary to those of the British Journal of Cancer provide for a multidisciplinary, but coherent forum for the publication of papers in cell and molecular biology, biophysics, molecular pharmacology and medicinal chemistry which identify a rational basis for therapeutic intervention. There are many potential cellular 'targets' of chemotherapy ranging from tumour cell DNA itself to critical enzyme pathways, oncogene products, and cell-surface associated antigens to mention but a few. Some of these themes are represented in the first issue: Polarographic analysis of calfthymus DNA shows that Platinum compounds with anti-tumour activity induce conformational changes which are different from those induced by Pt compounds with no anti-tumour effect; the potent biological activity of azaspiranes can be effected by compounds of this class other than those containing germanium, and include cytotoxic as well as potentially immunostimulatory effects; the bistriazenes, a novel type of triazene derivative, act as prodrugs for the cytotoxic monomethyltriazenes; the antitumour activity of actinomycin D analogues results in part from their ability to form reactive reduced oxygen species; ESR techniques may be used to study molecular events relevant to the lower general toxicity and cumulative cardiotoxicity of 4'epidoxorubicin than the widely clinically used doxorubicin, and antibody-targetted drug (methotrexate) carrier conjugates appear to involve a lysosomotropic mechanism of action rather than extracellular cleavage of the drug from its carrier. It is anticipated that future contributions, both theoretical and applied, will exploit new principles of structure-activity relationships and disclose new methods of visualising three-dimensional structures of drugs in relation to their receptors. Hopefully this approach will identify agents with selectivity for the products encoded by oncogenes. These proteins, which are essential to malignant behaviour provide ideal targets for chemotherapy because interference with them should arrest tumour growth. Supported by the Cancer Research Campaign and with an interdisciplinary Editorial Board of international standing, Anti Cancer Drug Design is a useful addition to the cancer chemotherapy literature in particular and to the oncology literature in general.

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Anti-Cancer Drug Design

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel corona virus that causes corona virus disease 2019 (COVID-19). The COVID-19 rapidly spread across the nations with high mortality rate even as very little is known to contain the virus at present. In the current study, we report novel natural metabolites namely, ursolic acid, carvacrol and oleanolic acid as the potential inhibitors against main protease (Mpro) of COVID-19 by using integrated molecular modeling approaches. From a combination of molecular docking and molecular dynamic (MD) simulations, we found three ligands bound to protease during 50 ns of MD simulations. Furthermore, the molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations showed that these chemical molecules have stable and favourable energies causing strong binding with binding site of Mpro protein. All these three molecules, namely, ursolic acid, carvacrol and oleanolic acid, have passed the ADME (Absorption, Distribution, Metabolism, and Excretion) property as well as Lipinski's rule of five. The study provides a basic foundation and suggests that the three phytochemicals, viz. ursolic acid, carvacrol and oleanolic acid could serve as potential inhibitors in regulating the Mpro protein's function and controlling viral replication. Communicated by Ramaswamy H. Sarma.

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Identification of phytochemical inhibitors against main protease of COVID-19 using molecular modeling approaches.

Background
Recent advances in high-throughput screening (HTS) techniques and readily available compound libraries generated using combinatorial chemistry or derived from natural products enable the testing of millions of compounds in a matter of days. Due to the amount of information produced by HTS assays, it is a very challenging task to mine the HTS data for potential interest in drug development research. Computational approaches for the analysis of HTS results face great challenges due to the large quantity of information and significant amounts of erroneous data produced.

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Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem

Informational entropy is quantitatively related to similarity and symmetry. Some tacit assumptions regarding their correlation have been shown to be wrong. The Gibbs paradox statement (indistinguishability corresponds to minimum entropy, which is zero) has been rejected. All their correlations are based on the relation that less information content corresponds to more entropy. Higher value of entropy is correlated to higher molecular similarity. The maximum entropy of any system (e.g., a mixture or an assemblage) corresponds to indistinguishability (total loss of information), to perfect symmetry or highest symmetry, and to the highest simplicity. This conforms without exception to all the experimental facts of both dynamic systems and static structures and the related information loss processes.

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Correlation of Entropy with Similarity and Symmetry

Two distinct approaches are possible in the area of computer-aided drug design. If the molecular structure of the target macromolecule is known the methods are obvious and direct and have achieved a high level of sophistication. That area may be extended by using computational techniques to predict protein structure as illustrated here by the interleukin-4 receptor. When the only lead is a set of known active compounds or knowledge of a biochemical transformation which is to be interrupted, then the path is less direct. Currently favoured tactics include the use of molecular similarity methods and the employment of neural networks. Recent advances include the prediction of the relative potency of different chiral forms of drugs.

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W. G. Richards

Papers

Computer-aided drug design

Computer-Aided Drug Design.