Chemical product design – recent advances and perspectives

Automated robotic platforms in design and development of formulations

“…35 To address the shortcomings of the group contribution-based methods, topological indices have been introduced, which are descriptors of the chemical structure to predict the physical properties of a molecule. 36 The obtained relationships are called quantitative structure property/ activity relationships (QSPR/QSAR). 37 These methods can take into account molecular information, such as the types of atoms and bonds, total number of atoms, and bonding between the atoms to predict physical properties; therefore, they play an important role in the design of large and complex molecules, such as pharmaceutical drugs, as they can capture the differences in conformations, isomers, or molecular structures.…”

“…42 Compared to knowledgebased models, data-driven surrogate models do not require prior knowledge; therefore ML-based models are finding increasing use to extract structure-property relationships, particularly in the cases of complex chemical formulations and materials. 36 The recent advances in molecular and material design using ML methods are summarized by Butler et al 43 Identifying suitable molecular descriptors for chemicals is still an open challenge for ML models, which may lead to further accuracy for chemical product property prediction. 44 In Figure 1, we illustrate the integration of the methodologies described in this article in the pre-existing theoretical framework reported by Zhang et al 36 Briefly, the market needs to define the product and its desired properties, that can be translated into quantifi-…”

“…However, one significant issue is that they rely on binary interaction parameters for every pair of groups in solution, often not available in thermodynamic properties databases 35 . To address the shortcomings of the group contribution‐based methods, topological indices have been introduced, which are descriptors of the chemical structure to predict the physical properties of a molecule 36 . The obtained relationships are called quantitative structure property/activity relationships (QSPR/QSAR) 37 .…”

“…Detailed review on those methods can be found in those references 22,29,40 . Furthermore, a systematic review on available computer‐aided methods and associated software tools for formulated product design can be found in Reference 36. Briefly, the model‐based approaches are able to efficiently find feasible candidates within the application range of the available models.…”

“…With the increase in computational power, data‐driven methods, such as ML‐based models, provide an alternative way for establishing the required process‐property models, in particular, when sufficient knowledge is not available 42 . Compared to knowledge‐based models, data‐driven surrogate models do not require prior knowledge; therefore ML‐based models are finding increasing use to extract structure–property relationships, particularly in the cases of complex chemical formulations and materials 36 . The recent advances in molecular and material design using ML methods are summarized by Butler et al 43 Identifying suitable molecular descriptors for chemicals is still an open challenge for ML models, which may lead to further accuracy for chemical product property prediction 44 .…”