Drug discovery is adapting to novel technologies such as data science,
informatics, and artificial intelligence (AI) to accelerate effective treatment
development while reducing costs and animal experiments. AI is transforming
drug discovery, as indicated by increasing interest from investors, industrial
and academic scientists, and legislators. Successful drug discovery requires
optimizing properties related to pharmacodynamics, pharmacokinetics, and
clinical outcomes. This review discusses the use of AI in the three pillars of
drug discovery: diseases, targets, and therapeutic modalities, with a focus on
small molecule drugs. AI technologies, such as generative chemistry, machine
learning, and multi-property optimization, have enabled several compounds to
enter clinical trials. The scientific community must carefully vet known
information to address the reproducibility crisis. The full potential of AI in
drug discovery can only be realized with sufficient ground truth and
appropriate human intervention at later pipeline stages.

药物发现正在适应新技术，如数据科学、信息学和人工智能（AI），以加速有效治疗的开发，同时减少成本和动物实验。 AI 正在改变药物发现的方式，成功的药物发现需要优化与药理动力学、药代动力学和临床结果相关的特性。本文讨论了 AI 在药物发现的三个支柱（疾病、靶点和治疗模式）中的应用，重点关注小分子药物。

药物发现领域的人工智能：现在已经到达了吗？

Artificial Intelligence for Drug Discovery: Are We There Yet?

Medical imaging informatics is a rapidly growing field that combines the
principles of medical imaging and informatics to improve the acquisition,
management, and interpretation of medical images. This chapter introduces the
basic concepts of medical imaging informatics, including image processing,
feature engineering, and machine learning. It also discusses the recent
advancements in computer vision and deep learning technologies and how they are
used to develop new quantitative image markers and prediction models for
disease detection, diagnosis, and prognosis prediction. By covering the basic
knowledge of medical imaging informatics, this chapter provides a foundation
for understanding the role of informatics in medicine and its potential impact
on patient care.

本文介绍医学影像信息学的基本概念，包括图像处理，特征工程和机器学习，并讨论了计算机视觉和深度学习技术的最新进展，以及它们如何用于开发用于疾病检测、诊断和预测模型的新量化图像标记。通过涵盖医学影像信息学的基础知识，为理解信息学在医学中的作用及其对患者护理的潜在影响提供了基础。

医学影像信息学导论

Introduction to Medical Imaging Informatics

Polymers are a vital part of everyday life. Their chemical universe is so
large that it presents unprecedented opportunities as well as significant
challenges to identify suitable application-specific candidates. We present a
complete end-to-end machine-driven polymer informatics pipeline that can search
this space for suitable candidates at unprecedented speed and accuracy. This
pipeline includes a polymer chemical fingerprinting capability called polyBERT
(inspired by Natural Language Processing concepts), and a multitask learning
approach that maps the polyBERT fingerprints to a host of properties. polyBERT
is a chemical linguist that treats the chemical structure of polymers as a
chemical language. The present approach outstrips the best presently available
concepts for polymer property prediction based on handcrafted fingerprint
schemes in speed by two orders of magnitude while preserving accuracy, thus
making it a strong candidate for deployment in scalable architectures including
cloud infrastructures.

一种快速高效的机器学习驱动的聚合物信息学管道，使用一种名为 polyBERT 的化学指纹能力来识别聚合物的化学结构，并将其映射到一系列物性。该管道通过 2 个数量级的速度优势远超过手工指纹方案，同时保持准确性，是部署在可扩展架构中的强有力选项。