Public articles linked to the same research event.
Journal of Chemical Information and Modeling Drawing on two case studies characterized by structural and biochemical data, this article examines how the close integration of computational and medicinal chemistry, together with a deep understanding of chemical shape and protein interactions, enabled targeted computational molecular design to produce disproportionate gains in efficacy and selectivity, and on that basis argues that current AI methods are sensitive to subtle, low-data perturbations, that AI must move beyond pattern recognition to understand or explicitly simulate the mechanistic "why" linking subtle structural changes to biological outcomes, and that until then AI is better positioned to complement human efforts than to serve as a stand-alone solution.
Drawing on two case studies characterized by structural and biochemical data, this article examines how the close integration of computational and medicinal chemistry, together with a deep understanding of chemical shape and protein interactions, enabled targeted computational molecular design to produce disproportionate gains in efficacy and selectivity, and on that basis argues that current AI methods are sensitive to subtle, low-data perturbations, that AI must move beyond pattern recognition to understand or explicitly simulate the mechanistic "why" linking subtle structural changes to biological outcomes, and that until then AI is better positioned to complement human efforts than to serve as a stand-alone solution.
Drawing on two case studies characterized by structural and biochemical data, this article examines how the close integration of computational and medicinal chemistry, together with a deep understanding of chemical shape and protein interactions, enabled targeted computational molecular design to produce disproportionate gains in efficacy and selectivity, and on that basis argues that current AI methods are sensitive to subtle, low-data perturbations, that AI must move beyond pattern recognition to understand or explicitly simulate the mechanistic "why" linking subtle structural changes to biological outcomes, and that until then AI is better positioned to complement human efforts than to serve as a stand-alone solution.
Drawing on two case studies characterized by structural and biochemical data, this article examines how the close integration of computational and medicinal chemistry, together with a deep understanding of chemical shape and protein interactions, enabled targeted computational molecular design to produce disproportionate gains in efficacy and selectivity, and on that basis argues that current AI methods are sensitive to subtle, low-data perturbations, that AI must move beyond pattern recognition to understand or explicitly simulate the mechanistic "why" linking subtle structural changes to biological outcomes, and that until then AI is better positioned to complement human efforts than to serve as a stand-alone solution.