Gordon Moore has bet against his own legislation several times; he has, however, each time been wrong. and biological systems requires scrutiny at spatial and temporal resolutions that challenge current experimental techniques. Molecular dynamics (MD) simulations are being increasingly partnered with experiments in this mission because simulations can track system behavior across a vast spatiotemporal domainlength scales up to thousands of ngstrms, with atomic precision, and timescales up to milliseconds, at femtosecond resolution. This power of simulations has been further increased by recent methodological advances. Here, we predict what the next 25 years of MD simulations may bring, especially regarding their application to the search for new drugs. Novel computational methods, including MD simulations, have assumed an ever growing role in drug discovery over the past quarter century. Yet, despite having learned and contributed much, we face many challenges ahead. To take novel computational methods to the next levelsuch that they radically alter the very scenery of drug discoverywe must grapple with those challenges and rise above them. This essay is meant to be thought provokingwe raise more questions than we answer. It is arranged such that the knowledgeable reader can easily skip to the parts of CANPL2 interest. We begin by reviewing the challenges, using any technique, including computational ones, in finding new drugs. Next, we compare the computational state of the art of 25 years ago with that of today, with a particular emphasis on MD simulations. Following brief comments on the nature of innovation and the art of making predictions, we make a series of straightforward predictions on the future directions of MD simulations, including Walrycin B particular ways simulations might be used in drug discovery; some key methodological improvements we believe will be needed for success are discussed. We then turn our attention to several Grand Challenges for the field, including a rather audacious goal on how simulations might be used in drug discovery. We conclude with our thoughts on how MD simulations are perceived by the larger scientific community, and on the importance of setting Walrycin B goals. == What makes finding a drug so difficult? == Computational chemistry methods have become deeply integrated into drug discovery over the past 25 Walrycin B years [1,2], expanding significantly beyond early work on quantitative structureactivity associations (QSAR) [3], computerized chemical structure representation [4], and computerized compound, reaction, house, and structureactivity databases [5]. Indeed, computational methods have become so much part of the very fabric from which new drugs are wovensimply ascertaining their specific impact is quite difficultthat their continued role in drug discovery has been questioned as the pharmaceutical industry faces looming scientific and economic challenges. It is thus prudent to inquire whether we are focusing our efforts where they yield the most benefit. Are we addressing the key challenges facing the pharmaceutical industry? Productivity is one of the most significant challenges facing the pharmaceutical industry, with $50 billion spent annually to produce only ~20 new drugs [6]. Ever-shifting organizational structures hamper success [79], but the root problem is simply that most new drug projects failonly ~3% of projects ever produce a marketed drug. It is crucial to understand at what stage in the pipeline projects fail, for what reasons, and then to inquire how computational approaches can help avert those failures. Drug discoverythe focus of most computational chemistry effortsis tolerably successful but needs improvement: about 35% of discovery projects succeed, on average, in delivering experimental drugs ready for clinical testing. The project stages of target identification and screening, hit-to-lead, lead optimization, and preclinical candidate selection have individual success rates ranging from 69 to 85% [10]. When discovery projects fail, they fail for diverse reasons, notably unclear target biology, lack of appropriate leads, poor potency or selectivity, inappropriate drug-like properties, lack of efficacy, and unexpected animal toxicity. Clinical development success rates, however, present.