Cell-penetrating peptides (CPPs) are powerful vectors for the intracellular
delivery of a diverse array of therapeutic molecules. Despite their potential,
the rational design of CPPs remains a challenging task that often requires
extensive experimental efforts and iterations. In this study, we introduce an
innovative approach for the de novo design of CPPs, leveraging the strengths of
machine learning (ML) and optimization algorithms. Our strategy, named
LightCPPgen, integrates a LightGBM-based predictive model with a genetic
algorithm (GA), enabling the systematic generation and optimization of CPP
sequences. At the core of our methodology is the development of an accurate,
efficient, and interpretable predictive model, which utilizes 20 explainable
features to shed light on the critical factors influencing CPP translocation
capacity. The CPP predictive model works synergistically with an optimization
algorithm, which is tuned to enhance computational efficiency while maintaining
optimization performance. The GA solutions specifically target the candidate
sequences' penetrability score, while trying to maximize similarity with the
original non-penetrating peptide in order to retain its original biological and
physicochemical properties. By prioritizing the synthesis of only the most
promising CPP candidates, LightCPPgen can drastically reduce the time and cost
associated with wet lab experiments. In summary, our research makes a
substantial contribution to the field of CPP design, offering a robust
framework that combines ML and optimization techniques to facilitate the
rational design of penetrating peptides, by enhancing the explainability and
interpretability of the design process.

该研究介绍了一种创新方法，结合机器学习和优化算法，通过 LightGBM 预测模型和遗传算法系统地生成和优化细胞穿透肽（CPP）序列，从而有效提高 CPP 的可解释性和可理解性。

LightCPPgen: 细胞穿透肽有理设计的可解释机器学习流程

LightCPPgen: An Explainable Machine Learning Pipeline for Rational  Design of Cell Penetrating Peptides

Despite substantial progress in machine learning for scientific discovery in
recent years, truly de novo design of small molecules which exhibit a property
of interest remains a significant challenge. We introduce LambdaZero, a
generative active learning approach to search for synthesizable molecules.
Powered by deep reinforcement learning, LambdaZero learns to search over the
vast space of molecules to discover candidates with a desired property. We
apply LambdaZero with molecular docking to design novel small molecules that
inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing
constraints on synthesizability and drug-likeliness. LambdaZero provides an
exponential speedup in terms of the number of calls to the expensive molecular
docking oracle, and LambdaZero de novo designed molecules reach docking scores
that would otherwise require the virtual screening of a hundred billion
molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable,
drug-like inhibitors for sEH. In in vitro experimental validation, a series of
ligands from a generated quinazoline-based scaffold were synthesized, and the
lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide
(UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

LambdaZero 利用深度强化学习探索大量分子空间，通过分子对接设计出抑制可溶性环氧解酯化酶 2 的新型小分子。在实验验证中，从生成的脒类新型小分子中选择的 UM0152893 表现出亚微摩尔级的酶抑制活性。

小分子蛋白质结合物搜索的生成式主动学习

Generative Active Learning for the Search of Small-molecule Protein  Binders

Engineering new molecules with desirable functions and properties has the
potential to extend our ability to engineer proteins beyond what nature has so
far evolved. Advances in the so-called "de novo" design problem have recently
been brought forward by developments in artificial intelligence. Generative
architectures, such as language models and diffusion processes, seem adept at
generating novel, yet realistic proteins that display desirable properties and
perform specified functions. State-of-the-art design protocols now achieve
experimental success rates nearing 20%, thus widening the access to de novo
designed proteins. Despite extensive progress, there are clear field-wide
challenges, for example in determining the best in silico metrics to prioritise
designs for experimental testing, and in designing proteins that can undergo
large conformational changes or be regulated by post-translational
modifications and other cellular processes. With an increase in the number of
models being developed, this review provides a framework to understand how
these tools fit into the overall process of de novo protein design. Throughout,
we highlight the power of incorporating biochemical knowledge to improve
performance and interpretability.

利用人工智能等方法进行蛋白质工程和全新设计的研究方案，如生成模型和扩散过程，能够生成展现出所需特性和功能的全新而真实的蛋白质，在优先测试设计方案方面仍然面临一些挑战，因此需要综合生物化学知识提高性能和可解释性。

用于全新蛋白质设计的生成式人工智能

Generative artificial intelligence for de novo protein design

De novo design seeks to generate molecules with required property profiles by
virtual design-make-test cycles. With the emergence of deep learning and neural
generative models in many application areas, models for molecular design based
on neural networks appeared recently and show promising results. However, the
new models have not been profiled on consistent tasks, and comparative studies
to well-established algorithms have only seldom been performed.
To standardize the assessment of both classical and neural models for de novo
molecular design, we propose an evaluation framework, GuacaMol, based on a
suite of standardized benchmarks. The benchmark tasks encompass measuring the
fidelity of the models to reproduce the property distribution of the training
sets, the ability to generate novel molecules, the exploration and exploitation
of chemical space, and a variety of single and multi-objective optimization
tasks. The benchmarking open-source Python code, and a leaderboard can be found
on this https URL

利用深度学习和神经生成模型实现了分子的设计，提出了基于标准化基准测试的评估框架，GuacaMol，可用于衡量分子的生成和性质分布等多方面。