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FREED++. Ускоряем поиск новых лекарств с помощью нейросетей

Привет! Меня зовут Александр Телепов, я — исследователь в Институте AIRI. Наша команда занимается применением глубокого обучения в науках о жизни. В сферу наших интересов входят такие задачи, как дизайн материалов, анализ растворимости или поиск новых лекарственных препаратов. Про последнее я бы хотел поговорить поподробнее.

О том, что сегодня для поиска новых соединений используют нейросети, слышали многие. Взять хотя бы нашумевший AlphaFold 3 от DeepMind, решающий задачу генерации трехмерной структуры разнообразных молекулярных комплексов. Существуют и другие задачи, в которых нейросети преуспели над классическими численными методами. Ярчайший пример — генерация молекул‑лекарств. Одним из самых заметных подходов к этой задаче стал фреймворк генерации молекул‑лекарств на основе методов обучения с подкреплением FREED. Но и он оказался далёк от идеала.

Не так давно наша научная группа воспроизвела, тщательно исследовала и существенно улучшила FREED. Мы представим свои результаты в журнале TMLR, статья доступна на архиве. Здесь же я кратко расскажу про сам FREED и его проблемы, а также суть наших исправлений этого подхода.

Разработка лекарственных препаратов

Сложно встретить сегодня человека, который никогда не принимал никакого лекарства. Точно так же сложно переоценить важность фармакологии и науки о разработке лекарственных препаратов. Для определенности мы будем называть лекарственными препаратами вещества, применяемые в профилактике, диагностике и лечении заболеваний.

Как правило, такая разработка происходит по схеме «лекарство‑мишень» в три этапа:

Сначала определяется мишень‑белок — ключевая молекула, вовлеченная в конкретный метаболический или сигнальный путь, связанный с определенным болезненным состоянием.
Определяется трехмерная структура белка (обычно методами кристаллографии).
Находится молекула с желаемым набором свойств, селективно взаимодействующая с белком‑мишенью.

Последняя задача является предметом изучения такой области как медицинская химия. В наиболее простом варианте основную задачу медицинской химии можно формализовать следующим образом:

Предложить химическое соединение (малую молекулу, лиганд), которое при связывании с интересующим белком существенно изменяет его функциональное состояние.

Количественно данный эффект выражается изменением энергии Гиббса для комплекса белок‑лиганд $\Delta G_{complex}$ . Оценка изменения энергии Гиббса — это отдельная сложная задача, которая решается методами молекулярного моделирования, как правило, методом молекулярного докинга. Отметим, что способность значительно изменять состояние конкретного белка‑мишени — это ключевое, но не единственное свойство вещества, определяющее возможность его использования как лекарственного препарата. При разработке лекарств следует учитывать множество других свойств: селективность образуемых взаимодействий (молекула должна избирательно взаимодействовать с белком, так как воздействие на другие белки в организме может вызвать нежелательные побочные эффекты), токсичность, реактивность, простоту синтеза и так далее.

Многофакторность проблемы привела исследователей к идее попробовать применить здесь методы глубокого обучения. В данном случае задача естественным образом формулируется в терминах генерации молекулярного графа, в узлах которого располагаются атомы, а рёбра соответствуют химическим связям. Один из DL‑подходов к такому построению — свести задачу к обучению с подкреплением, при условии, что мы умеем оценивать качество сгенерированного графа каким‑либо образом. Такой подход позволяет напрямую оптимизировать желаемые свойства молекулы, что хорошо соотносится с постановкой задачи из предыдущего параграфа. О RL и поговорим.

Обучение с подкреплением

Обучение с подкреплением — это фреймворк для оптимизации поведения агента в среде. Взаимодействие агента со средой происходит в замкнутом цикле. На каждой итерации взаимодействия агент, находясь в состоянии $s_t \in \mathcal{S}$ , выполняет действие $a_t \in \mathcal{A}$ . После выполнения действия среда переводит агента в состояние $s_{t+1}$ ( $s_{t+1} \sim \mathcal{P}(s_t, a_t)$ ) и выдает награду $r_t \in \mathbb{R}$ ( $r_{t} \sim \mathcal{R}(s_t, a_t, s_{t+1})$ ). Последовательность (s_0, a_0, r_0, s_1, a_1, r_1, ...) называется траекторией и обозначается $\mathcal{T}$ . Задача агента — найти стратегию $\pi(s_t)$ (правило, по которому сэмплируется $a_t \sim \pi(s_t)$ ), максимизирующую дисконтированную ожидаемую прибыль по всевозможным траекториям

$\mathbb{E}_{\mathcal{T} \sim \pi} \left[ \sum_{t=0}^{\infty} \gamma^{t} r_t \right] \xrightarrow[]{} \min_{\pi}.$

Формально, для постановки задачи обучения требуется определить кортеж ( $\mathcal{S}$ , $\mathcal{A}$ , $\mathcal{P}$ , $\mathcal{R}$ , $\gamma$ ), где $\mathcal{S}$ — пространство возможных состояний, $\mathcal{A}$ — пространство действий, $\mathcal{P}$ — функция динамики переходов, $\mathcal{R}$ — функция награды, $\gamma$ — коэффициент дисконтирования.

Данная задача хорошо изучена, и на сегодняшний день существует огромное количество методов ее решения. Для пространств состояний с большой размерностью и пространств со сложной структурой наиболее эффективно показали себя методы глубокого обучения с подкреплением, в которых функция ценности состояния (или состояния-действия) и/или стратегия агента параметризуются нейронными сетями.

Направленная генерация молекулярного графа

Как я уже отмечал выше, генерацию лигандов с желаемыми свойствами можно сформулировать в рамках фреймворка обучения с подкреплением. Для этого пространство состояний определяется как множество всевозможных молекулярных графов (отметим, что формально состояние также должно включать нотацию времени, оставшегося до конца эпизода), а действие, как правило, состоит из присоединения или удаления атома или молекулярного фрагмента.

Функцию награды можно ввести несколькими способами:

$r_{t+1} = J(s_{t+1}) - J(s_t)$

или

$r_{t+1}= \left\{ {\begin{array}{} 0 & если \ s_{t+1} - нетерминальное \ состояние; \\ J(s_{t+1}) & если \ s_{t+1} - терминальное \ состояние. \\ \end{array}} \right.$

где — свойство, которое нужно максимизировать. В простейшем случае в качестве можно взять некоторую оценку энергии связывания с конкретным белком (полученную, например, молекулярным докингом). Начинать генерацию можно со случайного, фиксированного или пустого фрагмента. Для обоих вариантов награды решение задачи обучения с подкреплением будет эквивалентно поиску лиганда с максимальной аффинностью (при $\gamma = 1$ ).

Схематически такой подход изображен на Рис.1.

Рис. 1. Схема генеративных методов, основанных на обучении с подкреплением. Агент принимает на вход текущее состояние и выбирает действие. Действие, как правило, состоит из присоединения молекулярного фрагмента к собранному на данный момент состоянию.

Отметим, что вплоть до появления модели FREED (то есть, конца 2021 года), в подавляющем большинстве работ по молекулярной генерации (CVAE, ORGAN, REINVENT, GraphVAE, MolGAN, JT‑VAE, GCPN) рассматривалась задача безусловной генерации, такой, в которой не принимается во внимание белок‑мишень (хотя были и исключения — LiGAN). Такие модели не являются оптимальными, поскольку терапевтический эффект молекулы существенно определяется её энергией связывания с целевым белком.

Метод FREED

В конце 2021 года на конференции NeurIPS был опубликован многообещающий подход генерации молекул-лекарств на основе методов обучения с подкреплением FREED. В отличии от большинства предыдущих работ, FREED учитывает биологическую мишень-белок при генерации лигандов. Кроме того, метод напрямую оптимизирует энергию связывания белка с лигандом. Отметим еще несколько полезных свойств FREED:

Фрагментная генерация существенно сокращает пространство возможных действий и состояний, позволяя эффективно обучать RL‑агента;
FREED работает над расширенным молекулярным графом — графом, в котором есть специальные вершины, точки присоединения (эти вершины определяют места, в которых были разорваны молекулярные связи в процессе фрагментации). Если присоединять фрагменты только к этим точкам, собираемая молекула будет автоматически удовлетворять правилам валентности при условии, что после терминального шага к молекуле будут добавлены недостающие водороды.

Результаты, описанные в статье FREED, действительно впечатляют. Однако в ходе проведения большого количества экспериментов и тщательного анализа исходного кода публикации мы выяснили, что предложенный метод имеет существенные ограничения и проблемы. А именно:

в существующей реализации присутствуют множественные баги;
протокол сравнения неконсистентен между предложенной моделью и бейзлайнами;
количество выбранных белков-мишеней для оценки качества недостаточно;
модель переусложнена и эффекты от ее отдельных частей недоисследованы.

От FREED к FFREED и FREED++

Чтобы избавиться от ограничений FREED мы тщательно исследовали исходный код публикации, исправили ошибки, провели дополнительные абляции, и упростили предложенную модель.

Перед тем, как описывать изменения внесенные в исходную модель, нужно более детально описать её саму. Однако поскольку исходная модель содержит в себе баги и неконсистентные блоки, это может ввести читателя в заблуждение. Поэтому я сначала опишу, как устроена исправленная модель FFREED (Fixed FREED), а затем приведу пример одного из найденных багов в модели.

FFREED

Рассмотрим, как устроен во FFREED выбор действия (архитектура политики, актор) и функция ценности состояния‑действия (критик).

Действие — это композиция трёх элементов. Первая компонента — это точка присоединения на молекуле, собранной к данному шагу. К этой точке будет присоединен новый фрагмент. Вторая компонента — фрагмент, который будет присоединен. Он выбирается из фиксированного словаря фрагментов, полученного в ходе процедуры фрагментации. И, наконец, третья компонента — точка присоединения на фрагменте.

Процесс выбора действия изображен на Рис. 2a.

Шаг 1: текущее состояние обрабатывается графовой сетью, и его эмбеддинг объединяется с эмбеддингами точками присоединения. После выбирается одна из точек присоединения a_1 , и ее эмбеддинг $\tilde{a}_1$ (более точно будет сказать эмбеддинг, обусловленный на эмбеддинг состояния) используется на следующем шаге.

Шаг 2: $\tilde{a}_1$ объединяется с эмбеддингами доступных фрагментов. Один из фрагментов выбирается как a_2 , и его эмбеддинг $\tilde{a}_2$ используется на следующем шаге (на самом деле выбор фрагмента устроен чуть более сложно, детальнее это описано в нашей статье).

Шаг 3: эмбеддинги точек присоединения выбранного фрагмента объединяются с $\tilde{a}_2$ . Наконец, одна из точек присоединения фрагмента выбирается как a_3 .

Критик: Эмбеддинги всех действий конкатенируются с эмбеддингом состояния и обрабатываются критиком (Рис. 2b).

Рис. 2. Схематические архитектуры актора и критика в модели FFREED.

Ниже мы опишем один из самых существенных багов, связанный с параметризацией критика (функции ценности состояния‑действия).

В оригинальной имплементации критик параметризован многослойным перцептроном, который принимает на вход конкатенацию 4 векторов:

1) эмбеддинга молекулы, полученного графовой нейронной сетью;

2) вектора вероятностей, ассоциированных с точками присоединения на молекуле;

3) one‑hot представления выбранного фрагмента;

4) вектора вероятностей, ассоциированных с точками присоединения на выбранном фрагменте.

В такой реализации есть две существенных проблемы. Во‑первых, критик не получает никакой информации о выбранных точках присоединения, так как оперирует только вероятностным распределением над этими точками. Во‑вторых, порядок, в котором сами вероятности подаются в критика, определяется внутренним представлением текущего состояния. Этот порядок может изменяться при присоединении новых фрагментов вдоль траектории. Таким образом, критик не может ассоциировать высокие награды за выбор определенных точек присоединения. Соответственно, задача выучить сколь бы то ни было разумную политику выбора точек присоединения становится неразрешимой. В нашей имплементации вместо векторов вероятностей мы подаем в критика эмбеддинги выбранных точек присоединения.

После исправления всех багов модель успешно справляется с оптимизацией награды, что хорошо видно на графике обучения, см. Рис. 3.

Рис. 3. Средняя награда (docking score) за эпизод в процессе обучения для белка USP7. Закрашенный регион обозначает 95-процентный доверительный интервал.

Кроме того, в абляциях мы исследовали, как устранение каждого бага к исходной модели FREED влияет на качество генерации. Оказалось, что исправление любого отдельного бага поодиночке не приводит к существенному улучшению качества — все ошибки необходимо исправлять совместно.

FFREED++

На следующем шаге мы значительно ускорили модель FFREED и уменьшили количество обучаемых параметров в ней. Получившуюся модель мы называли FREED++. Расскажу по порядку, что именно мы сделали.

Архитектура политики. Рассмотрим более детально, как устроена архитектура политики в моделях FREED и FFREED. Выбор действия происходит авторегрессионно, и каждая компонента действия a_i выбирается отдельной нейронной сетью. Устроены они однотипным образом, схематически изображенным на Рис. 4.

Рис. 4. Схема подсчета условной вероятности действий для FFREED и FREED++.

Каждая компонента политики вычисляет распределение вероятностей на множестве доступных опций при некотором условии. Например, для a_1 вычисляется вероятность выбора точки присоединения, при условии, что точка выбирается на молекуле, собранной к текущему шагу. Сначала эмбеддинг, ассоциированный с каждой опцией, сливается с эмбеддингом условия, например, эмбеддинги конкатенируются (на схеме функция слияния обозначена $f_{\phi}$ ). Затем эмбеддинг обусловленной опции обрабатывается многослойным перцептроном $f_{\psi}$ : $\mathbb{R}^{d} \xrightarrow{} \mathbb{R}$ , чтобы получить соответствующий логит. Наконец, условное распределение над опциями вычисляется оператором SoftMax (на самом деле, используется трюк Gumbel-Softmax, так как агент обучается методом SAC).

Функции слияния. Функция слияния (fusing functions) нужна для объединения нескольких эмбеддингов в один. В моделях FREED и FFREED в качестве функций слияния используются Multiplicative Interactions (MI). Пусть $x \in \mathbb{R}^{d_1}$ , $z \in \mathbb{R}^{d_2}$ представляют эмбеддинги, которые нужно объединить. Тогда, MI-слой примененный к и будет выглядеть следующим образом:

$\mathbf{f}^{MI}_{\phi_i}(x, z) = z^T \mathbf{W}_i x + \mathbf{U}_i z + \mathbf{V}_i x + \mathbf{b}_i,$

где $\mathbf{W} \in \mathbb{R}^{d_2 \times d_3 \times d_1}$ — это обучаемый трехмерный тензор, $\mathbf{U} \in \mathbb{R}^{d_3 \times d_2}$ , $\mathbf{V} \in \mathbb{R}^{d_3 \times d_1}$ — обучаемые матрицы, и $\mathbf{b} \in \mathbb{R}^{d_3}$ — смещение.

Отмечу, что MI‑слой содержит в себе вычисление билинейной формы, которое является дорогим с точки зрения времени и памяти.

Ускорение FFREED. Анализируя время вычисления различных частей политики, мы выяснили, что основное время тратится на шаге вычисления распределения вероятностей на множестве фрагментов. Это объясняется 3 факторами:

1) в качестве функций слияния используются MI‑взаимодействия;

2) входные эмбеддинги имеют высокую размерность (1024);

3) функцию слияния нужно вычислять раз, где — размерность словаря фрагментов (в оригинальной статье N=91 ).

Если набор опций фиксирован, то мы можем получить распределение над опциями альтернативным образом. Если — количество опций, то логиты можно получить непосредственно применив многослойный перцептрон $f_{\psi}$ : $\mathbb{R}^{d} \xrightarrow{} \mathbb{R}^{N}$ к эмбеддингу условия. В таком случае не требуется вычислять значение функции слияния ни разу, а вычисление $f_{\psi}$ требуется сделать всего один раз, вместо исходных раз.

В модели FREED++ мы заменили MI-слой на вычислительно более дешевую и часто используемую комбинацию конкатенации линейного слоя:

$\mathbf{f}^{CAT}_{\phi_i}(x, z) = \mathbf{Q}_i[x; z] + \mathbf{b}_i,$

Финальная модель FREED++, полученная после всех упрощений, оказалась быстрее в 8,5 раза и в 22 раза менее затратной по памяти, чем модель FFREED. Важно, что модель получилось ускорить без каких‑либо потерь в качестве генерации.

Таблица 1. Сравнение скорости FFREED и FREED++. Время обозначает количество секунд на 1 шаг градиентного спуска по батчу размера 100.

Сравнение с бейзлайнами

После исправления ошибок в исходном коде и оптимизации модели мы сравнили наш результат с другими существующими моделями на задаче генерации молекул с высокой аффинностью. В оригинальной работе протокол сравнения между бейзлайнами и моделью FREED неконсистентен, а набор тестовых белков недостаточен для аккуратного сравнения. В нашей работе мы расширили набор тестовых белков с 3 до 6, выполнили перебор гиперпараметров для бейзлайнов, добавили 2 метода для сравнения (комбинаторный генератор и Pocket2Mol, который и сегодня считается сильным бейзлайном), а также увеличили вычислительный бюджет для бейзлайн моделей (в оригинальной статье модели REINVENT и MolDQN были существенно недоучены).

Таблица 2. Сравнение FREED, FFREED и FREED++ с бейзлайнами на задаче оптимизации аффинности для белка USP7.

Наши результаты показывают, что модели FFREED и FREED++ способны генерировать молекулы с более высокими значениями аффинности, чем существующие подходы.

Заключение

Таким образом, у нас, кажется, получилось исправить, упростить и улучшить модель FREED, причём довольно неплохо. Однако будет лукавством заявлять, что мы достигли предела совершенства в задаче о генерации молекул‑лекарств методами глубокого обучения.

Как я уже упоминал RL — это лишь один из методов. Новые работы в области молекулярной генерации публикуются ежеквартально, и сегодня арсенал средств для поиска лекарств довольно велик.

На мой взгляд, наиболее интересными на данный момент являются диффузионные модели, восстанавливающие совместное распределение атомов малых молекул и белка. Однако они ограничены существующими датасетами, а также не способны (без дополнительных надстроек) оптимизировать энергию связывания, что является ключевым свойством лекарственного препарата. Кроме того, большинство из них работает только с небольшим куском (карманом) белка. Более реалистичный подход должен рассматривать весь белок, например, как это делается в AlphaFold 3.

Второй перспективный класс методов — генеративно‑потоковые нейронные сети. Их можно рассматривать как более эффективный вариант обучения с подкреплением, позволяющий генерировать более разнообразные молекулы с высокими значениями интересующих свойств. Однако и эти методы не лишены недостатков: разработка надежной функции награды, отражающей все желаемые свойства молекулы, является сложной задачей. Наиболее перспективным направлением, как мне кажется, станет разработка методов, комбинирующих эти два подхода.

Реализация FFREED и FREED++ доступна на нашем github, будем рады вашим звездочкам и форкам!

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12 сент. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
02:19 briankennedy

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