Benchling Molecular Biology

Benchling Molecular Biology is a purpose-built, cloud-native suite for sequence design and analysis that centralizes DNA, RNA and amino acid work in one collaborative environment. Designed for bench scientists and computational teams alike, it replaces ad hoc spreadsheets and disconnected tools with a single data model for biomolecules, plasmids, primers and associated experimental data. The product is used across academia, startups and large biopharma — helping teams standardize sequence registration, preserve audit trails, and reduce duplicate effort while accelerating iterative design cycles. The core capabilities cover the full in silico design lifecycle. An integrated sequence editor supports linear and plasmid views, feature/ORF annotation, auto-annotation via shared feature libraries, and automatic computation of biochemical properties. Users can import large sets of sequences from public databases, run BLASTn/BLASTp searches against internal repositories, and perform single or bulk alignments with an expanded set of algorithms. Design tools include primer optimization wizards, reverse complement/translation utilities, back-translation of proteins, and specialist support for chemically modified RNA and mRNA constructs. For cloning and construct assembly, Benchling provides guided wizards and virtual simulation for restriction digests, Gibson assembly, Golden Gate and homologous recombination strategies, plus a virtual digest tool to preview fragment maps and expected outcomes. Benchling also embeds advanced design and predictive capabilities. CRISPR guide RNA design is integrated with on- and off-target scoring, helping researchers prioritize guides. Protein structure prediction is available through AlphaFold2 integration to visualize and reason about 3D models next to sequence annotations. Antibody-specialized annotations — including frameworks and complementary-determining regions (CDRs) — are computed automatically for amino acid sequences. Teams can perform bulk operations at scale (bulk cloning, bulk export of primers to CSV for ordering, bulk annotation), and maintain version history so any change can be audited or reverted. Collaboration features include role-based permissions and sequence locking to prevent accidental edits, project-scoped search and advanced filters, and cross-linking of registered parts to full constructs so experimental data automatically ties back to canonical reagents. Practical use-cases span discovery, preclinical and process development. A typical workflow might start with importing candidate genes, annotating domains via a shared feature library, designing and optimizing primers, simulating a Gibson assembly, and then registering the final plasmid with attached experimental notes in Benchling’s ELN. Teams doing antibody discovery can combine sequence annotation, CDR mapping and structural models to prioritize variants; CRISPR projects can screen candidate guides with built-in scoring and then track edits and sequencing results directly against registered constructs. Benchling’s automation and workflow engines reduce repetitive tasks (for example, automating sequence registration or parsing sequencing ZIP files with a short Python script), freeing scientists to design more constructs per cycle and improving reproducibility across collaborators. Benchling is built to integrate with lab ecosystems and enterprise requirements. Out-of-the-box and custom integrations include a robust REST API and a Python SDK, app integrations, an interface designer for custom UIs, and instrument and LIMS connections to capture data automatically. The platform supports compliance needs — versioning, auditability, and GxP-compatible behaviors are available to teams preparing regulatory data packages. Admins can define granular access controls, create shared feature libraries to standardize naming and coloring of annotations, and use automated linking to ensure parts (promoters, tags, backbones) remain consistent across projects. With Benchling, organizations aim to scale molecular biology R&D by combining modern in silico design tools, collaboration, and a unified data foundation to increase throughput, reduce errors, and accelerate decision-making.