Pericyclic reactionIn organic chemistry, a pericyclic reaction is the type of organic reaction wherein the transition state of the molecule has a cyclic geometry, the reaction progresses in a concerted fashion, and the bond orbitals involved in the reaction overlap in a continuous cycle at the transition state. Pericyclic reactions stand in contrast to linear reactions, encompassing most organic transformations and proceeding through an acyclic transition state, on the one hand and coarctate reactions, which proceed through a doubly cyclic, concerted transition state on the other hand.
Reaction intermediateIn chemistry, a reaction intermediate or an intermediate is a molecular entity that is formed from the reactants (or preceding intermediates) but is consumed in further reactions in stepwise chemical reactions that contain multiple elementary steps. Intermediates are the reaction product of one elementary step, but do not appear in the chemical equation for an overall chemical equation. For example, consider this hypothetical stepwise reaction: A + B -> C + D The reaction includes two elementary steps: A + B -> X X -> C + D In this example, X is a reaction intermediate.
Organic synthesisOrganic synthesis is a special branch of chemical synthesis and is concerned with the intentional construction of organic compounds. Organic molecules are often more complex than inorganic compounds, and their synthesis has developed into one of the most important branches of organic chemistry. There are several main areas of research within the general area of organic synthesis: total synthesis, semisynthesis, and methodology.
Organic SynthesesOrganic Syntheses is a peer-reviewed scientific journal that was established in 1921. It publishes detailed and checked procedures for the synthesis of organic compounds. A unique feature of the review process is that all of the data and experiments reported in an article must be successfully repeated in the laboratory of a member of the editorial board as a check for reproducibility prior to publication. The journal is published by Organic Syntheses, Inc., a non-profit corporation.
Graph neural networkA graph neural network (GNN) is a class of artificial neural networks for processing data that can be represented as graphs. In the more general subject of "geometric deep learning", certain existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs. Convolutional neural networks, in the context of computer vision, can be seen as a GNN applied to graphs structured as grids of pixels. Transformers, in the context of natural language processing, can be seen as GNNs applied to complete graphs whose nodes are words in a sentence.
Danishefsky Taxol total synthesisThe Danishefsky Taxol total synthesis in organic chemistry is an important third Taxol synthesis published by the group of Samuel Danishefsky in 1996 two years after the first two efforts described in the Holton Taxol total synthesis and the Nicolaou Taxol total synthesis. Combined they provide a good insight in the application of organic chemistry in total synthesis. Danishefsky's route to Taxol has many similarities with that of Nicolaou. Both are examples of convergent synthesis with a coupling of the A and the C ring from two precursors.
Organic chemistryOrganic chemistry is a subdiscipline within chemistry involving the scientific study of the structure, properties, and reactions of organic compounds and organic materials, i.e., matter in its various forms that contain carbon atoms. Study of structure determines their structural formula. Study of properties includes physical and chemical properties, and evaluation of chemical reactivity to understand their behavior.
Attention (machine learning)Machine learning-based attention is a mechanism mimicking cognitive attention. It calculates "soft" weights for each word, more precisely for its embedding, in the context window. It can do it either in parallel (such as in transformers) or sequentially (such as recursive neural networks). "Soft" weights can change during each runtime, in contrast to "hard" weights, which are (pre-)trained and fine-tuned and remain frozen afterwards. Multiple attention heads are used in transformer-based large language models.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Language modelA language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.
RoboticsRobotics is an interdisciplinary branch of electronics and communication, computer science and engineering. Robotics involves the design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist humans. Robotics integrates fields of mechanical engineering, electrical engineering, information engineering, mechatronics engineering, electronics, biomedical engineering, computer engineering, control systems engineering, software engineering, mathematics, etc.
Neural networkA neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.
