Digital chemistry is the intersection of chemical science and digital technologies, where computational tools and algorithms are employed to simulate, understand, accelerate, and predict chemical processes and molecular behaviors (Figure ). This approach harnesses the power of computers to model reactions, design new molecules, and explore the vast chemical space more efficiently than traditional experimental methods alone. Digital chemistry encompasses several key areas, including computational chemistry, cheminformatics, [24] molecular modeling, virtual screening, machine learning, artificial intelligence applications, quantum computing, and dedicated software and devices such as electronic lab notebooks and robotics, respectively. It aims to accelerate discovery and innovation in various fields, such as drug development, material science, and sustainable chemistry, by providing insights that are difficult, if not impossible, to obtain through conventional laboratory experiments. A brief description of the eight key components of digital chemistry are: I) Computational Chemistry (since 1975 ): Understand the behavior of molecules and reactions at the atomic level Using quantum mechanical calculations and molecular dynamics simulations (see 4.1.). II) Cheminformatics (since 1998 ): Analyzing and managing large datasets of chemical compounds using computational techniques, such as data mining, to identify patterns and properties that can guide drug discovery and materials science (see 4.2.). III) Virtual screening (since 1989 ), together with experiment screening: Employing computational methods to screen vast libraries of chemical compounds for potential drug candidates or materials with desired properties, thereby reducing the time and cost of experimental screening (see 4.3.). IV) Molecular modeling (since 1970 ): Creating 3D models of molecules and materials to visualize their structure and properties and to optimize their design for specific applications (see 4.4.). V) Machine Learning (since 1990 ) and Artificial Intelligence (since 1972 ) in Chemistry: Utilizing machine learning algorithms, deep learning, and artificial intelligence techniques to predict molecular properties, optimize chemical reactions, and assist in designing new molecules and materials (see 4.5.). VI) Quantum Computing (since 1989 ): Exploring the potential of quantum computing to solve complex chemical problems more efficiently than classical computers, particularly for simulating quantum systems and optimizing molecular structures (see 4.6.). ,[34b] VII) Dedicated software tools : Streamline and enhance the documentation, management, and analysis of chemical experiments, data, and workflows, ensuring efficiency, compliance, and collaboration in scientific research (see 4.7). VIII) Chemical lab robotics, , , automatization, and selfdriving chemical labs : Streamline experimental processes by employing robotic systems and advanced algorithms to perform tasks such as sample preparation, analysis, and data interpretation with minimal human intervention (see 4.8.).