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A hardware-agnostic quantum ML framework with automatic differentiation, ideal for training quantum circuits via gradients, building hybrid quantum-classical models, and ensuring device portability across major quantum platforms. It's best for variational algorithms, quantum neural networks, and integration with PyTorch/JAX/TensorFlow.

Build and analyze phylogenetic trees using MAFFT, IQ-TREE 2, and FastTree. Visualize them with ETE3 or FigTree for applications in evolutionary analysis, microbial genomics, and molecular clock studies.

A fast in-memory DataFrame library for datasets that fit in RAM, ideal when pandas is too slow. It features lazy evaluation, parallel execution, and an Apache Arrow backend, making it suitable for 1-100GB datasets and ETL pipelines.

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.

PyHealth enables building deep-learning pipelines for healthcare, supporting the loading of diverse EHR/signal/imaging datasets, defining tasks like mortality prediction or drug recommendation, and training with various models.

A computational materials science toolkit for crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, and format conversion.

A complete mass spectrometry analysis platform for proteomics workflows, including feature detection, peptide identification, and protein quantification. It supports extensive file formats and algorithms, ideal for comprehensive MS data processing; for simple spectral comparison, use matchms.

Genomic file toolkit for NGS data processing pipelines. It reads/writes SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extracts regions, and calculates coverage.

Therapeutics Data Commons provides AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction.

Utilizes PyTorch Lightning to organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, and implement data pipelines, callbacks, logging, and distributed training for scalable neural network training.

IBM quantum computing framework. Use for IBM Quantum hardware, Qiskit Runtime production workloads, or IBM optimization tools, excelling in IBM hardware execution, quantum error mitigation, and enterprise quantum computing.

Cheminformatics toolkit for fine-grained molecular control, supporting SMILES/SDF parsing, descriptors, fingerprints, substructure search, 2D/3D generation, similarity, and reactions. For standard workflows with a simpler interface, use datamol (a wrapper around RDKit); for advanced control, custom sanitization, or specialized algorithms, use rdkit directly.