Computational Chemistry in the Pharmaceutical Industry: From Childhood to Adolescence.

Computational chemistry within the pharmaceutical industry has grown into a field that proactively contributes to many aspects of drug design, including target selection and lead identification and optimization. While methodological advancements have been key to this development, organizational developments have been crucial to our success as well. In particular, the interaction between computational and medicinal chemistry and the integration of computational chemistry into the entire drug discovery process have been invaluable. Over the past ten years we have shaped and developed a highly efficient computational chemistry group for small-molecule drug discovery at Bayer HealthCare that has significantly impacted the clinical development pipeline. In this article we describe the setup and tasks of the computational group and discuss external collaborations. We explain what we have found to be the most valuable and productive methods and discuss future directions for computational chemistry method development. We share this information with the hope of igniting interesting discussions around this topic.

One target-multiple indications: a call for an integrated common mechanisms strategy.

Ever-increasing research and development costs are putting constant pressure on the pharmaceutical industry to improve their efficiency. Efforts to increase the output of the research pipeline have yielded limited success. Traditionally, maximization of the value of a drug is attempted through life-cycle management, which is initiated late in development, or when the drug is already on the market. Validated targets can be exploited further through development of a follow-up drug, which may offer advantages regarding safety or convenience. In this article, we propose to systematically evaluate the full therapeutic potential of a drug target, proprietary chemical lead structure, or drug candidate as broad and as early as possible and we call this the 'common mechanism' approach.

Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits.

Drug-like and lead-like hits derived from HTS campaigns provide good starting points for lead optimization. However, too strong emphasis on potency as hit-selection parameter might hamper the success of such projects. A detailed absorption, distribution, metabolism, excretion and toxicology (ADME-Tox) profiling is needed to help identify hits with a minimum number of (known) liabilities. This is particularly true for drug-like hits. Herein, we describe how to break down large numbers of screening hits and we provide a comprehensive overview of the strengths and weaknesses for each structural class. The overall profile (e.g. ligand efficiency, selectivity and ADME-Tox) is the distinctive feature that will define the priority for follow-up.