# In Silico Study and Optimization of Molecular Nanomotors for Membrane Photopharmacology > **NIH NIH R16** · CALIFORNIA STATE UNIVERSITY LONG BEACH · 2023 · $147,500 ## Abstract Project Summary/Abstract There is a dire need in developing new molecular paradigms for pharmacotherapy to address problems of poor drug selectivity, causing side effects, drug resistance, and environmental toxicity. Currently, over 85 % of the drugs in clinical research are discarded due to poor selectivity. Increasing drug selectivity is a major concern of modern drug development. Photopharmacology increases drug selectivity by using controlled light-activation of drugs at a given time and location in the body. Recently, a light-driven molecular nanomotor has been developed (García- López et al., Nature, 548, 7669, 567, 2017), that is capable of disrupting biological membranes, inducing cell death in eucaryotic cells. This mechanism has potential applications in drug delivery through lipid nanoparticles, cancer treatment, and combating infectious diseases. Besides chemotherapy, radiation, and surgery, mechanical action of nanomotors on a molecular level could become a fourth modality in the treatment of patients. However, being still at a developmental stage, a detailed understanding of the molecular mechanism of this process is required to advance this technology towards clinical applications. We will use computer modeling to study the molecular mechanism of the membrane disruption by the recently developed nanomotor. Based on the gained insight, we will design and optimize new nanomotors by introducing functional groups to improve molecular prop- erties. The final goal is to develop a next generation of nanomotors that can be applied in clinical studies. To reduce phototoxicity, it is necessary that the motor operates with a high quantum yield, converting a high percent- age of the absorbed photons into mechanical work to displace membrane lipids. Furthermore, tissue applications require the irradiation wavelength to occur in the 600–1000 nm region, which penetrates deeper than the initially used ultraviolet light, that also has higher phototoxicity. We will employ computational methods based on quantum mechanics, molecular mechanics, and machine learning. Core of our study will be the real time simulation of the photoinduced dynamics of the nanomotor in the membrane, yielding atomistic information about the membrane disruption process. To this end we will use machine learning driven molecular dynamics. The machine learning algorithm will be trained using quantum mechanical simulations. Based on the gained insights, several molecular properties will be enhanced by modifications of the functional groups: a) binding affinity to the membrane; b) light absorption in the near infrared or visible region; c) absorption cross section and quantum yield. To obtain candidate molecules we will employ in silico high-throughput screening based on exhaustive molecule generation and machine learning of quantum mechanical properties. Candidates with improved properties will be synthe- sized by the García-López lab and studied experimentally to gauge the validity... ## Key facts - **NIH application ID:** 10629113 - **Project number:** 1R16GM149410-01 - **Recipient organization:** CALIFORNIA STATE UNIVERSITY LONG BEACH - **Principal Investigator:** Enrico Tapavicza - **Activity code:** R16 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $147,500 - **Award type:** 1 - **Project period:** 2023-06-01 → 2027-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10629113 ## Citation > US National Institutes of Health, RePORTER application 10629113, In Silico Study and Optimization of Molecular Nanomotors for Membrane Photopharmacology (1R16GM149410-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10629113. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*