Recent successes in virtual screening have been made possible by large models
and extensive chemical libraries. However, combining these elements is
challenging: the larger the model, the more expensive it is to run, making
ultra-large libraries unfeasible. To address this, we developed a
target-agnostic, efficacy-based molecule search model, which allows us to find
structurally dissimilar molecules with similar biological activities. We used
the best practices to design fast retrieval system, based on
processor-optimized SIMD instructions, enabling us to screen the ultra-large
40B Enamine REAL library with 100\% recall rate. We extensively benchmarked our
model and several state-of-the-art models for both speed performance and
retrieval quality of novel molecules.

通过开发目标不确定、以效能为基础的分子搜索模型，我们能在具有类似生物活性的分子中找到结构不相似的分子。我们使用最佳实践设计了一种快速检索系统，通过处理器优化的 SIMD 指令，可以在 100% 的召回率下筛选超大型的 Enamine REAL 化合物库，并对该模型及其他最先进模型进行了广泛的性能和检索质量的基准测试。

Bioptic - 一种基于疗效的无偏向目标的小分子搜索引擎

Bioptic -- A Target-Agnostic Efficacy-Based Small Molecules Search  Engine

We propose a novel computational strategy for de novo design of molecules
with desired properties termed ReLeaSE (Reinforcement Learning for Structural
Evolution). Based on deep and reinforcement learning approaches, ReLeaSE
integrates two deep neural networks - generative and predictive - that are
trained separately but employed jointly to generate novel targeted chemical
libraries. ReLeaSE employs simple representation of molecules by their SMILES
strings only. Generative models are trained with stack-augmented memory network
to produce chemically feasible SMILES strings, and predictive models are
derived to forecast the desired properties of the de novo generated compounds.
In the first phase of the method, generative and predictive models are trained
separately with a supervised learning algorithm. In the second phase, both
models are trained jointly with the reinforcement learning approach to bias the
generation of new chemical structures towards those with the desired physical
and/or biological properties. In the proof-of-concept study, we have employed
the ReLeaSE method to design chemical libraries with a bias toward structural
complexity or biased toward compounds with either maximal, minimal, or specific
range of physical properties such as melting point or hydrophobicity, as well
as to develop novel putative inhibitors of JAK2. The approach proposed herein
can find a general use for generating targeted chemical libraries of novel
compounds optimized for either a single desired property or multiple
properties.

该论文提出了一种新的计算策略，名为 ReLeaSE（增强学习结构进化），通过深度学习和强化学习的方法，利用生成模型和预测模型生成具有预设属性的新型化合物库，此方法可用于生成具有单一或多个属性的有针对性的化合物库。