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A new type of big science is emerging that involves knowledge integration and collaboration among small sciences. Because open collaboration involves participants with diverse motivations and interests, social dynamics have a critical role in making the project successful. Thus, proper 'social engineering' will have greater role in scientific project planning and management in the future.
Because of the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development.
As biochemistry ventures out from its reductionist roots, concentration effects and high surface-to-volume ratios will challenge our current understanding of biological systems, with colloidal and surface chemistry leading to new insights and approaches. How must our thinking change, what new tools will we need and how will these new tools be developed?
The pharmaceutical industry is in a period of crisis due to the low number of new drug approvals relative to the high levels of R&D investment. It is argued here that improving the quality of target selection is the single most important factor to transform industry productivity and bring innovative new medicines to patients.
Unsupported assumptions have no place in scientific research, so why should they be tolerated when judging the talents and motivations of women in science?
