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Biotech and life sciences
virtual-screening
drug virtual screening
docking pipelines
System
Techniques · 5
Primitives · 23
virtual-screening
Graph neural networks
machine-learning · medium: Message-passing neural networks over graphs (GCN, GAT, equivariant) for molecules and relations
Genetic algorithms
optimization · medium: Population search with selection, crossover and mutation for black-box optimization
Ligand conformer generation
physics · medium: Generate 3D molecule conformers via distance geometry and torsion sampling
Molecular docking scoring
physics · medium: Pose search and empirical scoring functions for ligand-protein docking
HNSW
search-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
›
L-BFGS
numerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.
›
Force field terms
physics · low: Bonded and nonbonded MD potentials: bonds, angles, torsions, Lennard-Jones, Coulomb with cutoffs
Molecular graph representation
physics · low: SMILES parsing, molecular graphs, aromaticity and canonical ordering
Spatial hashing
physics · low: Uniform grid stored in a hash table: bucket objects by cell, test only neighbouring cells; O(n) neighbour search for many similar-sized objects such as particles.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
›
Spline interpolation
numerics · low: Linear, bilinear/trilinear and cubic spline interpolation on grids and tables
Molecular fingerprints similarity
physics · low: Circular (ECFP/Morgan) bit fingerprints and Tanimoto similarity with popcount
Attention variants
machine-learning · low: Multi-head, multi-query, grouped-query, sliding-window and latent attention; same softmax(QKᵀ/√d)V core, different trade-offs between quality and KV-cache size.
›
Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
›
Normalization layers
machine-learning · low: Standardize activations per batch, token or group with a learned scale so deep nets train fast and stably; RMSNorm in LLMs, BatchNorm in convnets.
›
Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
›
Alias method sampling
numerics · low: Vose alias tables: O(1) sampling from a discrete distribution after O(n) setup
Kabsch rmsd alignment
geometry · low: Optimal rigid superposition of point sets by SVD; RMSD computation
+ 7 more primitives
System
Techniques · 5
Primitives · 23
virtual-screening
Graph neural networks
machine-learning · medium: Message-passing neural networks over graphs (GCN, GAT, equivariant) for molecules and relations
Genetic algorithms
optimization · medium: Population search with selection, crossover and mutation for black-box optimization
Ligand conformer generation
physics · medium: Generate 3D molecule conformers via distance geometry and torsion sampling
Molecular docking scoring
physics · medium: Pose search and empirical scoring functions for ligand-protein docking
HNSW
search-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Top-k selection
search-sort · low: Keep the k best of n items without sorting all n - bounded min-heap for streams, quickselect for arrays, threshold pruning to skip most items.
›
L-BFGS
numerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.
›
Force field terms
physics · low: Bonded and nonbonded MD potentials: bonds, angles, torsions, Lennard-Jones, Coulomb with cutoffs
Molecular graph representation
physics · low: SMILES parsing, molecular graphs, aromaticity and canonical ordering
Spatial hashing
physics · low: Uniform grid stored in a hash table: bucket objects by cell, test only neighbouring cells; O(n) neighbour search for many similar-sized objects such as particles.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
›
Spline interpolation
numerics · low: Linear, bilinear/trilinear and cubic spline interpolation on grids and tables
Molecular fingerprints similarity
physics · low: Circular (ECFP/Morgan) bit fingerprints and Tanimoto similarity with popcount
Attention variants
machine-learning · low: Multi-head, multi-query, grouped-query, sliding-window and latent attention; same softmax(QKᵀ/√d)V core, different trade-offs between quality and KV-cache size.
›
Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
›
Normalization layers
machine-learning · low: Standardize activations per batch, token or group with a learned scale so deep nets train fast and stably; RMSNorm in LLMs, BatchNorm in convnets.
›
Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
›
Alias method sampling
numerics · low: Vose alias tables: O(1) sampling from a discrete distribution after O(n) setup
Kabsch rmsd alignment
geometry · low: Optimal rigid superposition of point sets by SVD; RMSD computation
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
›
Scalar quantization
search-sort · low: Per-dimension int8/binary quantization of vectors with rescoring
Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
›
AdamW
ml-training · low: Adam's per-parameter adaptive steps with weight decay applied directly to the weights; the default optimizer for transformers and LLM pretraining.
›
Rejection sampling
ml-training · low: Best-of-n and filtered sampling: generate many candidates, keep those passing a verifier or reward threshold
Reverse-mode automatic differentiat…
ml-training · low: Record the forward computation on a tape, then sweep it backwards once to get the gradient of a scalar loss for all inputs.
›
Parallel prefix sum
search-sort · low: Work-efficient scan (Blelloch, decoupled look-back) on CPU SIMD and GPUs; stream compaction
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