Niklas Rindtorff

I am building tools for cyclic peptide binder design at convexity labs. A future I want to see become reality is one in which the cost to design a new molecule with a specific biochemical function is almost too cheap to meter through the use of high-fidelity computational and in-vitro models.

Previously, I developed a microscopy-based, small molecule screening platform using patient derived cancer organoids. This approach can be used to discover new therapeutics, or to prioritise treatment options for cancer patients.

I completed my MD thesis with summa cum laude under the supervision of Michael Boutros at the German Cancer Research Center in Heidelberg. With the support of the Fulbright Program, I earned a Masters degree in biomedical informatics under the supervision of Jesse Boehm at the Broad Institute of MIT and Harvard.

Cycle-0: An Inversion-based Cyclic Peptide Generator

Cyclic peptides (ca. 150 heavy atoms) sit between small molecules (ca. 15 heavy atoms) and proteins (ca. 1500+ heavy atoms) in medicinal chemical space. Given their size and composability they are tractable molecules for both physical simulations, and generative modeling approaches. Recently, model-inversion based generative design methods, such as Bindcraft, have enabled the design of high-affinity protein-based binders. Can a similar approach be used to design cyclic peptides, and does the smaller size of these binders allow us to perform higher fidelity simulations of their binding properties in-silico, thus improving the design proccess? In this project, I am developing Cycle0, an inversion based design approach for cyclic peptides which outperforms current diffusion-based generative modelling methods in in-silico designability metrics.

A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using in vitro Data

Assigning cancer patients to different treatment options based on the genetic makeup of their tumor can be understood as a policy learning problem. What methods allow us to find strong policies while minimizing regret? In this small project, which I presented at NEURIPS 2019, we created a benchmark dataset for treatment policy learning from in vitro cancer cell line drug sensitivity data.

The drug-induced phenotypic landscape of colorectal cancer organoids

Over the last century, we've slowly accumulated a collection of ca. 1000 publicly available cancer cell lines. Recently, new approaches such as 3D patient-dervied organoids have lowered the cost of establishing new in-vitro cancer models considerably, putting us on track to double the number of cancer models within the decade. But what determines the structural organisation of these multicelluar models? In this project, we identified two distinct molecular factors, IGFR1 and Wnt-signaling, which can be manipulated to control cancer organoid structure and modify treatment response. These factors can guide future therapeutic discovery and tissue engineering.