Results for “chemberta”

Featurização molecular para ML (100+ featurizadores). ECFP, MACCS, descritores, modelos pré-treinados (ChemBERTa), converter SMILES em features, para QSAR e ML molecular.

NOXA Fun/DEX collab for Cheshire Terminal — launch feed, DEX registry, QuoterV2 quotes, EVM RPC. One-shot npm package @x402solana/cheshire-noxa.

Migrate .NET projects from per-project package versioning to NuGet Central Package Management (CPM) using Directory.Packages.props, with baseline build capture, version conflict resolution, and post-conversion validation.

Runs NV-Segment-CTMR segmentation on CT or MRI NIfTI volumes and records label-map evidence.

Use when searching DNA or RNA queries against nucleotide sequence databases with HMMER's nucleotide homology search engine.

Calculates first- and second-order option Greeks (Delta, Gamma, Theta, Vega, Rho, Charm, Vanna, Volga) for single contracts or multi-leg positions, with scenario heatmaps and position-level aggregation.

Build trading, liquidity provision, and pool management applications on Solana and Eclipse using the Orca Whirlpools SDK, covering swaps, positions, and fee harvesting.

Query ChEMBL via the chembl_webresource_client Python client for curated bioactive molecules and drug-like compound libraries at scale — search compounds by structure or physicochemical properties, retrieve bioactivity measurements (IC50, Ki, EC50), and find inhibitors of a target. Use when screening chemical libraries, mining curated bioactivity for a protein, running SAR studies, or sourcing medicinal-chemistry data; for measured protein-ligand binding affinities (Ki/Kd/IC50) prefer alterlab-bindingdb instead. Part of the AlterLab Academic Skills suite.

Training Compute-Optimal Large Language Models

Use when searching one or more protein query sequences against a protein sequence database with HMMER's one-pass sequence-vs-sequence searcher.

CHEETAH v2.0 — HYPE Predator. SM commitment as primary signal. Top performer at +7.6%. All fleet fixes applied.

Develops and backtests quantitative trading strategies for the Chinese securities market using the QMT terminal's built-in Python framework, covering data retrieval, order placement, and position management.

COBRA v1.1 — Arena Sprint Predator. Trades the single most dominant SM asset with maximum conviction. Fee-optimized maker orders. Wider DSL lets positions breathe. Designed to win Arena weeks.

Use when building a cursor trail whose spacing stays constant at any hand speed by emitting motes per distance travelled (not on a timer)—sub-segment placement, ring-buffer ordering, idle breath, 3D screen anchoring, coasting, touch and reduced-motion fallbacks—for wisps, sparks, embers, comet tails, plankton, or dust.

MAMBA v2.0 — Range-bound high water + regime protection. A trading strategy config override based on the VIPER skill with three protective gates: BTC regime filter, per-asset cooldown after losses, and hard leverage cap at 10x.

Provides guidance on using the QMT quantitative trading terminal, including strategy development, backtesting, and live trading for Chinese securities markets.

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

当用户明确要求“解决当前 merge / rebase / cherry-pick 冲突、提交代码、创建 commit、把改动分批提交并 push、推送当前分支”时使用。进行中的冲突要先识别 Git 操作和双方原始意图，逐 hunk 做语义合并并运行项目检查，再经用户确认继续操作；普通改动要检查敏感信息与异常差异，拆成可运行、可审查、可回滚的原子提交后确认提交和推送。不得自动发起或中止 merge / rebase / cherry-pick，不得强推或随意改写已发布历史。不要用于普通改动尚未完成或验证失败、只想查看 Git 状态，或要创建 PR 的场景。

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

You are an expert in Chi, the lightweight, idiomatic Go HTTP router built on `net/http`. You help developers build composable HTTP services using Chi's middleware stack, route groups, URL parameters, sub-routers, and context-based request scoping — providing Express-like ergonomics while staying 100% compatible with Go's standard library.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.