Research
From force fields to foundation models for DNA.
Tools & platforms
Software, one project page at a time
Each of these has its own page with the full story, a live link where one exists, and the papers it's tied to.
PhD Research
Arizona State University · Prof. Petr Šulc
Machine Learning & AI Development
PyTorch, TensorFlow, LangChain, CuPy, Transformers, GCNN, Boltzmann Machines, Clustering, Molecular embedding, OpenCV
- Created a Scalable Interpolant Transformer with SE(3)-invariant coordinates to generate equilibrated 3D conformations of DNA/RNA from sequence inputs, achieving atomic-level structural accuracy.
- Extended the transformer architecture to produce relaxed 3D DNA origami structures from line diagrams, enabling biophysics-informed design of complex nanostructures.
- Designed oxView LLM, fine-tuning the Qwen model for end-to-end DNA/RNA structure generation, relaxation, simulation, and analysis through natural language prompts.
- Improved DNA-PAINT super-resolution microscopy to 1nm resolution using DBSCAN clustering algorithms for 3D origami geometry deconvolution.
- Integrated oxDNA simulation with ML algorithms to uncover detailed nucleic acid dynamics and folding pathways.
- Accelerated AFM image analysis of DNA origami using OpenCV-based automated feature detection.
- Developed an LLM-based autograder using LangChain for ASU Physics experimental coursework, reducing grading time by 60% while improving feedback consistency.
- Built fastDNA: a flow-matching generative model (SE(3)-equivariant transformer, Clifford geometric algebra attention) for generating equilibrium 3D DNA/RNA conformations; trained on HPC with PyTorch Lightning and Weights & Biases; model sizes 2M–70M parameters.
- Developed PhysicsGrader / Graviton: a production LLM grading system (LangChain, Next.js, Canvas LMS API) serving 395 students across 10 lab courses; implemented hallucination/factuality checks, RBAC, MFA, and data encryption (ASU compliance).
- Built NanoCanvas: a cadnano2-inspired DNA origami design tool with a full LLM agent REST API (FastAPI, React, Three.js) enabling programmatic structural design via Model Context Protocol (MCP).
- Developed RAG-backed retrieval pipelines using LangChain, FAISS vector database, and Hugging Face embeddings for domain-specific scientific document search and context-aware LLM responses.
Molecular Simulation & Force Field Development
Developer of oxDNA and oxView, AMBER MD, GROMACS, OpenMM, C++, CUDA, Force field development, Cython
- Active core developer of oxDNA and oxView: implemented non-singular mass support, modern GPU compatibility (CUDA), and ARM architecture optimization.
- Co-developed algorithms for inverse design of 3D polycube architectures, determining minimal building block sets and interaction rules for target geometries.
- Built custom CUDA-optimized code to simulate unprecedented system sizes, scaling molecular dynamics simulations to ~6 million particles.
- Formulated protein-DNA hybrid system (PROTAC) denaturation pathways using quantum-classical all-atom simulations for targeted protein degradation.
- Developed a novel coarse-grain force field for irregular origami shapes using rigid body physics and patchy particle models, achieving a 1000× speedup over oxDNA2.
- Enhanced the patchy particle force field for DNA-mediated crystal growth with improved dynamics and phase behavior prediction.
- Achieved experimental validation: increased DNA origami crystal growth temperature by 12°C with higher yield based on simulation-guided design.
- Designed DNA icosahedron-helix crystal systems with optical properties at visible wavelengths for photonic applications.
- Built oxCloud: a unified platform for simulation, analysis, and hosting of DNA origami structures, simplifying the complete simulation pipeline for experimentalists.
- Developed a Babylon.js-based 3D visualization platform for non-standard coarse-grain molecular models.
- Fitted SAXS experimental results with simulation using constrained GROMACS simulation.
Computational Infrastructure & Experimental Validation
SLURM, Linux Kernel, Docker, Kubernetes, DNA purification, AFM, DNA-PAINT, PCR
- Set up and maintain Linux HPC clusters with x86-64 and ARM64-v8 CPUs and NVIDIA GPUs using the SLURM workload manager.
- Hands-on experience with experimental techniques: DNA gel electrophoresis, PCR, DNA-PAINT super-resolution microscopy, AFM imaging.
- Validated computational predictions through wet-lab experiments, closing the loop between simulation and experimental design.
Master's Thesis
IIIT Hyderabad · Prof. Deva Priyakumar
Binding Affinity Prediction
PyTorch, CNN, AMBER MD, MM-PBSA, MM-GBSA, AutoDock Vina, Molecular docking
- Generated the PLAS-5k dataset: 5,000 protein-ligand complexes with binding affinities for ML training (published in Scientific Data).
- Developed a CNN model achieving a Pearson correlation coefficient of 0.96 in 10-fold cross-validation.
- Automated all-atom MD simulations and extracted binding parameters (MM-PBSA/MM-GBSA), outperforming AutoDock Vina in accuracy and speed.
- Published an open-access dataset enabling broader community research in drug discovery.
Summer Research
IISER Kolkata · Prof. Neelanjana Sengupta
Viral Protein Dynamics
NAMD, Conformational entropy analysis, SASA calculations, Structural persistence analysis
- Investigated allosteric destabilization of the Zika virus NS1 protein β-ladder domain through disulfide bond reduction.
- Performed extensive NAMD simulations, analyzing conformational entropy, enthalpy, SASA, and structural persistence.
- Identified biochemical pathways for NS1 destabilization, informing structure-based drug design strategies (published in Biophysical Journal).