Teodoro Laino

Humans use different domain languages to represent, explore, and communicate scientific concepts…

With advances in computational models for retrosynthetic analysis in recent years [1-4], it is now possible to routinely propose reliable synthetic routes for target molecules of interest…

Accelerated discovery of novel chemical structures with desired properties is a grand challenge of our time…

Deep-learning models applied to chemical reactions have received much attention in recent years:

We present a data-driven approach for formulations of novel materials via autoencoders-based models. This work is driven by the opportunity to change the way to develop new recipes in the material industry from a heuristic to a data-driven approach…

We present a data-driven approach for formulations of novel materials via autoencoders-based models. This work is driven by the opportunity to change the way to develop new recipes in the material industry from a heuristic to a data-driven approach…

In the quest to build better automatic retrosynthetic tools, the ability to interface artificial intelligence models with a more traditional computational chemistry software becomes of paramount importance…

Designing and making new molecules is one of the most impactful outcomes of chemistry. The use of domain knowledge accumulated over multiple decades of laboratory experience, has been a crucial element for the synthesis of many new molecular structures…

Artificial intelligence is driving one of the most important revolutions in organic chemistry…

Accelerated discovery of novel chemical structures with desired properties is a grand challenge of our time…

Deep-learning models applied to chemical reactions have received much attention in recent years:

The first decade of genome sequencing saw a surge in the characterisation of proteins with unknown functionality. Even still, more than 20% of proteins in well-studied model animals have yet to be identified, making the discovery of their active site one of biology's greatest difficulties…

In single-step retrosynthesis, a target molecule is broken down by considering the bonds to be changed and/or functional group interconversions. In modern computer-assisted synthesis planning tools, the predictions of these changes are typically carried out automatically…

Enzyme catalysts are an integral part of green chemistry strategies towards a more sustainable and resource-efficient chemical synthesis…

In single-step retrosynthesis, a target molecule is broken down by considering the bonds to be changed and/or functional group interconversions…

A substantial increase in the availability of computational resources and open access chemical data sets has led to a rise of interest in computer-aided synthesis planning and the construction of biochemical reaction networks…

Finding feasible multistep synthesis pathways is key to accelerating the discovery process in synthetic organic chemistry…

With few exceptions, the majority of machine learning research on computer assisted synthesis and forward reaction prediction has used datasets of chemical reaction records derived from USPTO patents…

The need for more efficient and sustainable catalysts is ever-growing, and so are the costs associated with experimentally searching the chemical space to find a new promising catalyst. Computational methods, such as DFT, allow for screening molecules virtually for their suitability. B. Meyer et al…

The computation of the charge density is a ubiquitous and time-consuming task in large scale electronic structure computations, e.g in Nanodevice Simulations, Density Functional Theory and the solution of Self-Consistent Field equations…

The first decade of genome sequencing saw a surge in the characterisation of proteins with unknown functionality. Even still, more than 20% of proteins in well-studied model animals have yet to be identified, making the discovery of their active site one of biology's greatest difficulties…

Neural language models have achieved impressive results in various natural language understanding and generation tasks…

Chemical reactions can be classified into distinct categories that encapsulate concepts for how one molecule is transformed into another. One can encode these concepts in rules specifying the set of atoms and bonds that change during a transformation, which is commonly known as a reaction template…

With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently…

The last decade has witnessed a growing interest in applying machine learning (ML) tools to traditional sciences such as chemistry…

Chemical reaction rules have been used for a variety of applications including the codification and classification of chemistry, synthetic route design, and virtual library enumeration…

Machine learning algorithms demonstrated remarkable accuracy in predicting the outcomes of chemical reactions, often outperforming human experts. Simultaneously, a high level of precision was achieved in the single-step retrosynthesis prediction problem…

Enzymes are molecular engines that nature has designed to allow otherwise impossible chemical reactions to take place. They present exceptional properties, making them appealing for more sustainable reactions: mild conditions, fewer toxic solvents, and less waste…

Neural language models have achieved impressive results in various natural language understanding and generation tasks…

: [CINF] Division of Chemical Information

Chemical reactions can be classified into distinct categories that encapsulate concepts for how one molecule is transformed into another. One can encode these concepts in rules specifying the set of atoms and bonds that change during a transformation, which is commonly known as a reaction template…

: [CINF] Division of Chemical Information

With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently…

The last decade has witnessed a growing interest in applying machine learning (ML) tools to traditional sciences such as chemistry…

: [CINF] Division of Chemical Information

Neural language models have achieved impressive results in various natural language understanding and generation tasks…

: [CINF] Division of Chemical Information

Chemical reactions can be classified into distinct categories that encapsulate concepts for how one molecule is transformed into another. One can encode these concepts in rules specifying the set of atoms and bonds that change during a transformation, which is commonly known as a reaction template…

: [CINF] Division of Chemical Information

With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently…

The last decade has witnessed a growing interest in applying machine learning (ML) tools to traditional sciences such as chemistry…

: [CINF] Division of Chemical Information

Chemical reaction rules have been used for a variety of applications including the codification and classification of chemistry, synthetic route design, and virtual library enumeration…

: [COMP] Division of Computers in Chemistry

Machine learning algorithms demonstrated remarkable accuracy in predicting the outcomes of chemical reactions, often outperforming human experts. Simultaneously, a high level of precision was achieved in the single-step retrosynthesis prediction problem…

Neural language models have achieved impressive results in various natural language understanding and generation tasks…

: [CINF] Division of Chemical Information

Chemical reactions can be classified into distinct categories that encapsulate concepts for how one molecule is transformed into another. One can encode these concepts in rules specifying the set of atoms and bonds that change during a transformation, which is commonly known as a reaction template…

: [CINF] Division of Chemical Information

With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently…

The last decade has witnessed a growing interest in applying machine learning (ML) tools to traditional sciences such as chemistry…

: [CINF] Division of Chemical Information

Chemical reaction rules have been used for a variety of applications including the codification and classification of chemistry, synthetic route design, and virtual library enumeration…

: [COMP] Division of Computers in Chemistry

Machine learning algorithms demonstrated remarkable accuracy in predicting the outcomes of chemical reactions, often outperforming human experts. Simultaneously, a high level of precision was achieved in the single-step retrosynthesis prediction problem…

Neural language models have achieved impressive results in various natural language understanding and generation tasks…

Chemical reactions can be classified into distinct categories that encapsulate concepts for how one molecule is transformed into another. One can encode these concepts in rules specifying the set of atoms and bonds that change during a transformation, which is commonly known as a reaction template…

With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently…

The last decade has witnessed a growing interest in applying machine learning (ML) tools to traditional sciences such as chemistry…

Chemical reaction rules have been used for a variety of applications including the codification and classification of chemistry, synthetic route design, and virtual library enumeration…

Machine learning algorithms demonstrated remarkable accuracy in predicting the outcomes of chemical reactions, often outperforming human experts. Simultaneously, a high level of precision was achieved in the single-step retrosynthesis prediction problem…

Enzymes are molecular engines that nature has designed to allow otherwise impossible chemical reactions to take place. They present exceptional properties, making them appealing for more sustainable reactions: mild conditions, fewer toxic solvents, and less waste…

The application of machine learning models in chemistry has made remarkable strides in recent years. From enhancing retrosynthesis over expediting DFT calculations to predicting new drug candidates, the field has seen immense progress…

The right solvent is a crucial factor in achieving environmentally friendly, selective, and highly converted chemical reactions…

Learning from laboratory data at scale poses a bottleneck in research workflows…

Structure elucidation is integral in the day-to-day operation of any organic chemistry laboratory, providing crucial insights into the composition and purity of diverse substances…

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often overlooked…

Polymers have versatile properties and a wide range of applications. The optimization of polymeric materials and the development of new polymers are, however, time-consuming processes…

Reaching optimal reaction conditions is crucial to achieve high yields, minimize by-products, and ensure environmentally sustainable chemical reactions…

In chemistry, the prevalence of negative data is a significant yet often overlooked facet of research. Despite improvements in experimental design, it is still believed that unsuccessful experimental outcomes (negative data) are almost one order of magnitude larger than positive results…